On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning

The dissemination of hateful memes online has adverse effects on social media platforms and the real world. Detecting hateful memes is challenging, one of the reasons being the evolutionary nature of memes; new hateful memes can emerge by fusing hateful connotations with other cultural ideas or symbols. In this paper, we propose a framework that leverages multimodal contrastive learning models, in particular OpenAI's CLIP, to identify targets of hateful content and systematically investigate the evolution of hateful memes. We find that semantic regularities exist in CLIP-generated embeddings that describe semantic relationships within the same modality (images) or across modalities (images and text). Leveraging this property, we study how hateful memes are created by combining visual elements from multiple images or fusing textual information with a hateful image. We demonstrate the capabilities of our framework for analyzing the evolution of hateful memes by focusing on antisemitic memes, particularly the Happy Merchant meme. Using our framework on a dataset extracted from 4chan, we find 3.3K variants of the Happy Merchant meme, with some linked to specific countries, persons, or organizations. We envision that our framework can be used to aid human moderators by flagging new variants of hateful memes so that moderators can manually verify them and mitigate the problem of hateful content online.Comment: To Appear in the 44th IEEE Symposium on Security and Privacy, May 22-25, 202

Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models

State-of-the-art Text-to-Image models like Stable Diffusion and DALLE$\cdot$2 are revolutionizing how people generate visual content. At the same time, society has serious concerns about how adversaries can exploit such models to generate unsafe images. In this work, we focus on demystifying the generation of unsafe images and hateful memes from Text-to-Image models. We first construct a typology of unsafe images consisting of five categories (sexually explicit, violent, disturbing, hateful, and political). Then, we assess the proportion of unsafe images generated by four advanced Text-to-Image models using four prompt datasets. We find that these models can generate a substantial percentage of unsafe images; across four models and four prompt datasets, 14.56% of all generated images are unsafe. When comparing the four models, we find different risk levels, with Stable Diffusion being the most prone to generating unsafe content (18.92% of all generated images are unsafe). Given Stable Diffusion's tendency to generate more unsafe content, we evaluate its potential to generate hateful meme variants if exploited by an adversary to attack a specific individual or community. We employ three image editing methods, DreamBooth, Textual Inversion, and SDEdit, which are supported by Stable Diffusion. Our evaluation result shows that 24% of the generated images using DreamBooth are hateful meme variants that present the features of the original hateful meme and the target individual/community; these generated images are comparable to hateful meme variants collected from the real world. Overall, our results demonstrate that the danger of large-scale generation of unsafe images is imminent. We discuss several mitigating measures, such as curating training data, regulating prompts, and implementing safety filters, and encourage better safeguard tools to be developed to prevent unsafe generation.Comment: To Appear in the ACM Conference on Computer and Communications Security, November 26, 202

Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models

State-of-the-art Text-to-Image models like Stable Diffusion and DALLE·2 are revolutionizing how people generate visual content. At the same time, society has serious concerns about how adversaries can exploit such models to generate problematic or unsafe images. In this work, we focus on demystifying the generation of unsafe images and hateful memes from Text-to-Image models. We first construct a typology of unsafe images consisting of five categories (sexually explicit, violent, disturbing, hateful, and political). Then, we assess the proportion of unsafe images generated by four advanced Text-to-Image models using four prompt datasets. We find that Text-to-Image models can generate a substantial percentage of unsafe images; across four models and four prompt datasets, 14.56% of all generated images are unsafe. When comparing the four Text-to-Image models, we find different risk levels, with Stable Diffusion being the most prone to generating unsafe content (18.92% of all generated images are unsafe). Given Stable Diffusion’s tendency to generate more unsafe content, we evaluate its potential to generate hateful meme variants if exploited by an adversary to attack a specific individual or community. We employ three image editing methods, DreamBooth, Textual Inversion, and SDEdit, which are supported by Stable Diffusion to generate variants. Our evaluation result shows that 24% of the generated images using DreamBooth are hateful meme variants that present the features of the original hateful meme and the target individual/community; these generated images are comparable to hateful meme variants collected from the real world. Overall, our results demonstrate that the danger of large-scale generation of unsafe images is imminent. We discuss several mitigating measures, such as curating training data, regulating prompts, and implementing safety filters, and encourage better safeguard tools to be developed to prevent unsafe generation

On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning

The dissemination of hateful memes online has adverse effects on social media platforms and the real world. Detecting hateful memes is challenging, one of the reasons being the evolutionary nature of memes; new hateful memes can emerge by fusing hateful connotations with other cultural ideas or symbols. In this paper, we propose a framework that leverages multimodal contrastive learning models, in particular OpenAI’s CLIP, to identify targets of hateful content and systematically investigate the evolution of hateful memes. We find that semantic regularities exist in CLIP-generated embeddings that describe semantic relationships within the same modality (images) or across modalities (images and text). Leveraging this property, we study how hateful memes are created by combining visual elements from multiple images or fusing textual information with a hateful image. We demonstrate the capabilities of our framework for analyzing the evolution of hateful memes by focusing on antisemitic memes, particularly the Happy Merchant meme. Using our framework on a dataset extracted from 4chan, we find 3.3K variants of the Happy Merchant meme, with some linked to specific countries, persons, or organizations. We envision that our framework can be used to aid human moderators by flagging new variants of hateful memes so that moderators can manually verify them and mitigate the problem of hateful content online

Global brain analysis of minor hallucinations in Parkinson’s disease using EEG and MRI data

IntroductionVisual hallucination is a prevalent psychiatric disorder characterized by the occurrence of false visual perceptions due to misinterpretation in the brain. Individuals with Parkinson’s disease often experience both minor and complex visual hallucinations. The underlying mechanism of complex visual hallucinations in Parkinson’s patients is commonly attributed to dysfunction in the visual pathway and attention network. However, there is limited research on the mechanism of minor hallucinations.MethodsTo address this gap, we conducted an experiment involving 13 Parkinson’s patients with minor hallucinations, 13 Parkinson’s patients without hallucinations, and 13 healthy elderly individuals. We collected and analyzed EEG and MRI data. Furthermore, we utilized EEG data from abnormal brain regions to train a machine learning model to determine whether the abnormal EEG data were associated with minor hallucinations.ResultsOur findings revealed that Parkinson’s patients with minor hallucinations exhibited excessive activation of cortical excitability, an imbalanced interaction between the attention network and the default network, and disruption in the connection between these networks. These findings is similar to the mechanism observed in complex visual hallucinations. The visual reconstruction of one patient experiencing hallucinations yields results that differ from those observed in subjects without such symptoms.DiscussionThe visual reconstruction results demonstrated significant differences between Parkinson’s patients with hallucinations and healthy subjects. This suggests that visual reconstruction techniques may offer a means of evaluating hallucinations

Analysis of Qiaojia earthquake disasters in the Zhaotong area: Reasons for “small earthquakes and major disasters”

In May 2020, a magnitude 5.0 earthquake occurred in Qiaojia County, Yunnan. This earthquake was characteristics of relatively common “small earthquakes and major disasters” in the Zhaotong area of northeastern Yunnan. Yunnan province is an area in China with a high incidence of moderately strong earthquakes with magnitude 5 or above. In this region, the unique geological structure background and geographical environment of the Zhaotong area make it the most severely damaged area not only in Yunnan but also in all of China. The geological structure of the Zhaotong area is complex, and the neotectonic movements are strong. Based on the disasters of 8 moderate and strong earthquakes in Zhaotong since 2000, this paper analyses the causes of the “small earthquakes and major disasters” in Zhaotong city and provides suggestions for disaster reduction and emergency response after an earthquake

Polyaniline/Carbon Black Composite as Pt Electrocatalyst Supports for Methanol Oxidation: Synthesis and Characterization

Research Fund for the Doctoral Program of Higher Education [20070384047]; Natural Science Fund of Fujian Province of China [E0310003, E0610029]; Scientific and Technical Project of Fujian Province of China [2006I0026, 2007F3088]; Scientific and TechnicalCore/shell composites of polyaniline (PANI) and Vulcan XC-72 Carbon (VC), in which the carbon represents the core and PANI forms the shell, were synthesized by in situ chemical oxidation polymerization. Platinum (Pt) particles were then deposited on the PANI/VC composites by chemical reduction method. The highest conductivity is obtained when a mass ratio of PANI/VC equals to 0.28, as proved by Fourier transform infrared spectra. And it is also proved that there are some reactions happened between PANI and VC. Scanning electron microscope, transmission electron microscope, and X-ray diffraction measurements were performed to analyze their structure and surface morphology. It has been observed that the Pt particles are smaller in size and more uniformly distributed on these composite supports than on pure VC supports, considered as a reference. Methanol oxidation performed on the electrode modified by such a composite catalyst has been measured by cyclic voltammogram focusing on the attenuation of methanol oxidation current after 200 cycles. The attenuation degree for the composite catalyst is only one-third of the one measured for a simple Pt/VC catalyst. It is proved that the composite catalyst better resist carbon monoxide poisoning in comparison with the Pt/VC catalyst, which may be due to the synergetic effects between the composite support and the Pt catalyst. (C) 2010 Wiley Periodicals, Inc. J Appl Polym Sci 118: 2039-2042, 201

Stimulator of Interferon Genes Promotes Host Resistance Against Pseudomonas aeruginosa Keratitis

Pseudomonas aeruginosa (PA) is the leading cause of bacterial keratitis, especially in those who wear contact lens and who are immunocompromised. Once the invading pathogens are recognized by pattern recognition receptors expressed on the innate immune cells, the innate immune response is stimulated to exert host defense function, which is the first line to fight against PA infection. As a converging point of cytosolic DNA sense signaling, stimulator of interferon genes (STING) was reported to participate in host–pathogen interaction. However, the role of STING in regulating PA-induced corneal inflammation and bacterial clearance remains unknown. Our data demonstrated that STING was activated in murine model of PA keratitis and in in vitro-cultured macrophages, indicated by Western blot, immunostaining, and flow cytometry. To explore the role of STING in PA keratitis, we used siRNA to silence STING and 2′,3′-cGAMP to activate STING in vivo and in vitro, and the in vivo data found out that STING promoted host resistance against PA infection. To investigate the reason why STING played a protective role in PA keratitis, the inflammatory cytokine secretion and bacterial load were measured by using real-time PCR and bacterial plate count, respectively. Our data demonstrated that STING suppressed the production of inflammatory cytokines and enhanced bacterial elimination in murine model of PA keratitis and in PA-infected macrophages. To further investigate the mechanism beneath, the phosphorylation of mitogen-activated protein kinase, the nuclear translocation of nuclear factor-κB (NF-κB) and the bactericidal mechanism were measured by western-blot, immunofluorescence, and real-time PCR, respectively. Our data indicated that STING suppressed inflammatory cytokine expressing via restraining NF-κB activity and enhanced inducible NO synthase expression, an oxygen-dependent bactericidal mechanism. In conclusion, this study demonstrated that STING promoted host resistance against PA keratitis and played a protective role in PA-infected corneal disease, via inhibiting corneal inflammation and enhancing bacterial killing

Triggering Receptors Expressed on Myeloid Cells 2 Promotes Corneal Resistance Against Pseudomonas aeruginosa by Inhibiting Caspase-1-Dependent Pyroptosis

Triggering receptors expressed on myeloid cells 2 (TREM2) is a novel cell surface receptor and functions as an immunomodulatory receptor in infectious diseases. In this study, we investigated the function and regulatory mechanism of TREM2 in Pseudomonas aeruginosa (P. aeruginosa) keratitis. We found that P. aeruginosa keratitis was more severe in Trem2−/− versus wild type C57BL/6 mice as indicated by the increased clinical scores, bacterial load, and cornea pathology. The exacerbated disease progression caused by TREM2 deficiency was associated with boosted activation of caspase-1 and subsequent pyroptosis as well as increased expression of IL-1β. In addition, blockage of pyroptosis by caspase-1 inhibitor not only recovered the severe cornea pathology developed in Trem2−/− mice but also restored the P. aeruginosa clearance suppressed by TREM2 deficiency. Our study demonstrated that TREM2 promotes host resistance against P. aeruginosa keratitis by inhibiting caspase-1-dependent pyroptosis, which provides new insights of TREM2-mediated anti-bacterial immunity

Association of Parasomnia Symptoms with Risk of Childhood Asthma and the Role of Preterm Birth

Purpose: To examine whether parasomnia symptoms are associated with increased odds of childhood asthma and wheeze, and the role of preterm birth. Patients and Methods: The Shanghai Children’s Allergy Study was cross-sectionally conducted in 31 kindergartens and 17 primary schools in Shanghai, China. After excluding the missing data of gestational week and child’s age, this study included a total of 16,487 individuals with a mean age of 7.74 years and 52.4% of males. The association between parasomnia symptoms and wheeze/asthma was assessed by univariate and multivariate analyses. The interaction effects of parasomnias and preterm birth were primarily evaluated by P for multiplicative interaction, and the relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (SI) were also measured. Results: Parasomnias, especially rapid eye movement (REM) parasomnia symptoms, were associated with an increased risk of childhood wheeze/asthma, and the interaction between parasomnia and preterm birth exhibited an excess risk of current wheeze (RERI, 1.43; 95% CI, 0.41–2.45) and ever asthma (RERI, 0.75; 95% CI, 0.01–1.50). In the stratification analyses, the combination of parasomnia symptoms and preterm birth had higher odds of wheeze/asthma. And the odds of current wheeze (OR, 4.55; 95% CI, 1.69–12.25; p=0.003) and ever asthma (OR, 6.17; 95% CI, 2.36–16.11; p<0.001) were much higher in cumulative parasomnia symptoms plus very preterm birth. And sensitive analyses were further conducted in populations without sleep disordered breathing (SDB), and an allergen test subgroup, yielding similar results. Conclusion: Parasomnia symptoms are associated with increased odds of childhood wheeze/asthma, and the odds were even higher in premature population. The findings suggest that parasomnia symptoms, as a developmental sleep disorder, are supposed to be closely watched among children who have asthma or are at risk for asthma, and that preterm children deserve more attention.</p