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Downloads May 26: Glaze hits 721,000 downloads 10 weeks after initial release.April 16: updated Glaze GPU version for Windows available for download.March 31: Glaze Beta3 (Windows/Mac) now available for download.Glaze generates a cloaked version for each image you want to protect. During this process, none of your artwork will ever leave your own computer. Then, instead of posting the original artwork online, you could post the cloaked artwork to protect your style from AI art generators. We will not commercialize our protection tool inany way. It is available to use for free uponrelease. It is solely for research purposes, with thegoal of protecting artists.If you are interested to hear about news updateson the application release, please join this Glaze-announce mailing list. What Is Glaze? Glaze is a tool to help artists to prevent their artistic styles from being learned and mimicked by new AI-art models such as MidJourney, Stable Diffusion and their variants. It is a collaboration between the University of Chicago SAND Lab and members of the professional artistcommunity, most notably Karla Ortiz. Glaze has been evaluatedvia a user study involving over 1,100 professional artists.At a high level, here's how Glaze works: Suppose we want to protect artist Karla Ortiz's artwork in her online portfolio from being taken by AI companies and used to train models that can imitate Karla'sstyle.Our tool adds very small changes to Karla's original artwork before it is posted online. These changes are barely visible to the human eye, meaning that the artwork still appears nearly identical to the original, while still preventing AI models from copying Karla's style. We refer to these added changes as a "style cloak" and changed artwork as"cloaked artwork." For example, Stable Diffusion today can learn to create images in Karla's style after it sees just a few pieces of Karla's original artwork (taken from Karla's online portfolio). However, if Karla uses our tool to cloak her artwork, by adding tiny changes before posting them on her online portfolio, then Stable Diffusion will not learn Karla's artistic style. Instead, the model will interpret her art as a different style (e.g., that of Vincent van Gogh). Someone prompting Stable Diffusion to generate "artwork in Karla Ortiz's style" would instead get images in the style of Van Gogh (or some hybrid). This protects Karla's style from being reproduced without her consent. You can read our research paper (currently under peer review). With Glaze (above), a model trains on cloaked versions of Karla's art, and learns a different style from her original visual style. When it is asked to mimic Karla, it produces art that is distinctively different from Karla's style. Selected media coverage of our project:

One of the possible strategies is represented by designing analogue peptides in which specific amino acid residues are substituted to improve their antigenicity and immunogenicity (heteroclitic peptides) [10,11,12,13]. However, the exploitation of homology between TAAs and antigens derived from microorganisms (MoAs) (molecular mimicry) may provide the most effective results. Indeed, they are abundantly accessible natural non-self-antigens, which do not need any manipulation for improving antigenity or immunogenicity.

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Apart from the homology between TCR facing residues of two epitopes, the molecular mimicry would not play any role in human diseases if T-cell receptors (TCRs) would not be able to cross-react with different peptides when complexed to MHC (pMHC complexes).

Increasing but not definitive evidences support the concept that the molecular mimicry, originally described playing a role in the pathogenesis of autoimmune diseases, is relevant in shaping the anti-tumor T cell repertoire.

The possible role of the molecular mimicry and cross-reactive T-cells in the spontaneous regression of tumors has been proposed but never experimentally demonstrated [59, 60]. Moreover, several studies have reported that gut microbiome influences the response to immune checkpoint inhibitors (ICIs) but also the survival in pancreatic cancer after surgery [61,62,63,64,65,66]. However, none of these studies has supported these observations with experimental data confirming molecular mimicry of microbiota-derived antigens and TAA as well as cross-reactive T cells.

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Our general perspective in this review is compatible with recent reviews that emphasize the modulation of mimicry by social context (Duffy and Chartrand 2015; Fischer and Hess 2017; Hess and Fischer 2013). However, we put more emphasis on how such modulation reflects an interaction of basic and higher-order cognitive and affective processes, and elaborate on specific mechanisms. Furthermore, we also emphasize the cognitive functions of imitation and link spontaneous mimicry to basic mechanisms of emotion memory, emotion recognition, and emotion understanding. Finally, we also focus on the modulatory role of perceptual similarity and non-cognitive variables, such as reward processing and bodily sensitivity, and its atypical operation in autistic and lonely individuals. As such, our perspective highlights that whereas some cases of mimicry modulation are driven by higher-order cognitive and social goals, other cases are better understood as reflecting perceptual and affective processes, potentially leading to non-optimal or even maladaptive mimicry modulation.

Having said that, it is clear that more work needs to be done to fully understand the boundary conditions for the role of facial mimicry in emotion recognition. In fact, we are far from proposing that mimicry is always involved in the processing of facial expressions (or that it always necessary). For instance, in some studies observers mimicked emotional faces, but the degree of mimicry was not correlated with decoding accuracy (Blairy et al. 1999). However, when the task is more conceptually complex, as in perceiving authenticity of a smile, fEMG activity did predict judgments (Korb et al. 2014).

Further, Calder et al. (2000) found that three patients with Mobius syndrome (a congenital condition that causes facial paralysis, thus preventing mimicry) correctly categorized emotional faces, with impairments only at high levels of recognition difficulty. Another study found that individuals with Mobius syndrome do not differ in facial emotion-recognition accuracy compared to controls (Rives-Bogart and Matsumoto 2010), though such individuals also had a life to learn alternative recognition strategies. Similarly, as discussed more fully below, autistic participants who show atypical mimicry may also develop alternative routes to recognition. Here, the critical point is that typical perceivers may activate sensorimotor networks, when appropriate, in the course of everyday processing. Such activations can be useful for recognition, especially when the recognition cannot be achieved via a highly-automated pattern-recognition strategy. To be clear, motor activation is probably not needed to recognize a large teeth-bearing smile, but could be quite useful in subtler recognition with subtle, ambiguous, or brief expressions (i.e., much of real social life).

Autism Spectrum Conditions (ASC) represent a constellation of symptoms characterized by atypicalities in three general areas: (i) social interaction, such as atypical social interest and behavior, (ii) communicative skills, including pragmatic language and gestures, and (iii) behavioral atypicalities, as with the presence of restricted or stereotyped patterns of behaviors, interests, and activities. Critically, autism is a multifaceted disorder. There is no widely-accepted biological cause of autism, and there is no single causal explanation, at any level, that explains all aspects of the syndrome. Further, the behavioral manifestations of this disorder vary in severity (e.g., low and high-functioning ASCs) and heterogeneity of the cognitive profile (e.g., language, intelligence, emotion, etc.). Unfortunately, different profiles of ASCs are often lumped together when reporting findings, sometimes obscuring the information about the level and domain of functioning for which the findings are relevant. Given that most research studies are conducted on high-functioning ASC individuals, they are most informative about this sub-group. Thus, it is worth keeping in mind that research across the spectrum of symptomology may reveal differential, idiosyncratic relationships amongst variables and outcomes. However, there do appear to be some consistent underlying mechanisms contributing to social challenges in ASC, such as spontaneous mimicry and reward processing.

Note that these and related findings may reflect basic differences in attention to social stimuli as well as motivation and expertise (Southgate and de Hamilton 2008; Wang and de Hamilton 2012). When such factors are tightly controlled, it is possible to create conditions where ASC individuals can demonstrate intact spontaneous mimicry of hand actions (Bird et al. 2007) and facial expressions (Press et al. 2010; also see Cook et al. 2014b).

There is also evidence linking reduced spontaneous mimicry to atypical processing of social reward (Chevallier et al. 2012; Dichteret al. 2012; Kohls et al. 2012; Scott-Van Zeeland et al. 2010). This idea was tested in a psychophysiological study, which found that spontaneous facial mimicry was modulated by the reward value of different facial identities (Sims et al. 2012). In this experiment, neutral faces were first conditioned with high and low rewards using an evaluative conditioning task, instantiated through a card game. In the test phase, participants saw happy and angry expressions made by these same faces, while fEMG was recorded from congruent muscles to measure spontaneous facial mimicry. More rewarding faces were associated with greater spontaneous mimicry of joy, compared to less rewarding faces. Crucially, this reward-dependent modulation of spontaneous smile mimicry was inversely related to autistic traits (i.e., individuals high in autistic traits showed little facilitation of spontaneous mimicry for high versus low rewarding faces). An extension of these findings, using the same evaluative conditioning paradigm, showed that autistic traits did not moderate the extent of (implicit) conditioning (as measured by an implicit association test), but did reduce subsequent prosocial behavior toward the conditioned high-reward identities (Panasiti et al. 2016). This is especially interesting in the context of previous reports linking mimicry and prosocial behavior (van Baaren et al. 2004).