Quantifying aesthetics of visual design applied to automatic design

In today\u27s Instagram world, with advances in ubiquitous computing and access to social networks, digital media is adopted by art and culture. In this dissertation, we study what makes a good design by investigating mechanisms to bring aesthetics of design from realm of subjection to objection. These mechanisms are a combination of three main approaches: learning theories and principles of design by collaborating with professional designers, mathematically and statistically modeling good designs from large scale datasets, and crowdscourcing to model perceived aesthetics of designs from general public responses. We then apply the knowledge gained in automatic design creation tools to help non-designers in self-publishing, and designers in inspiration and creativity. Arguably, unlike visual arts where the main goals may be abstract, visual design is conceptualized and created to convey a message and communicate with audiences. Therefore, we develop a semantic design mining framework to automatically link the design elements, layout, color, typography, and photos to linguistic concepts. The inferred semantics are applied to a design expert system to leverage user interactions in order to create personalized designs via recommendation algorithms based on the user\u27s preferences

Web pages: What can you see in a single fixation?

Research in human vision suggests that in a single fixation, humans can extract a significant amount of information from a natural scene, e.g. the semantic category, spatial layout, and object identities. This ability is useful, for example, for quickly determining location, navigating around obstacles, detecting threats, and guiding eye movements to gather more information. In this paper, we ask a new question: What can we see at a glance at a web page – an artificial yet complex “real world” stimulus? Is it possible to notice the type of website, or where the relevant elements are, with only a glimpse? We find that observers, fixating at the center of a web page shown for only 120 milliseconds, are well above chance at classifying the page into one of ten categories. Furthermore, this ability is supported in part by text that they can read at a glance. Users can also understand the spatial layout well enough to reliably localize the menu bar and to detect ads, even though the latter are often camouflaged among other graphical elements. We discuss the parallels between web page gist and scene gist, and the implications of our findings for both vision science and human-computer interaction.Google Awar

The relationships between emotional intelligence, sensation seeking, and marital satisfaction among female teachers

Background: Marriage and matrimony is an issue widely affected by emotional intelligence and sensation seeking. The purpose of the present study was to investigate the relationships between emotional intelligence, sensation seeking, and marital satisfaction among female teachers in the city of Borujerd.Methods: The statistical population consisted of all married female teachers in the city of Borujerd among whom 100 were selected as the sample using random cluster sampling method. To collect the data, Enrich marital satisfaction, Bar-On emotional intelligence, and Zuckerman sensation seeking questionnaires were used. Data analysis was performed using SPSS software running Pearson’s correlation test and stepwise regression.Results: The results of the analysis indicated that, with a significance level of P<0.001, there was a negative and significant relationship between sensation seeking and marital satisfaction, and there was a positive and significant relationship between emotional intelligence and marital satisfaction. Also, a statistically significant relationship was found between sensation seeking and emotional intelligence. Due to the inverse relationship, any increase in sensation seeking scores results in a decrease in marital satisfaction and vice versa. Moreover, because of the direct relationship between the two variables, any increase in emotional intelligence results in an increase in marital satisfaction.Conclusion: Considering the findings of the present study, sensation seeking and emotional intelligence have a significant relationship with marital satisfaction. The results of the study can help psychologists and counselors to pay more attention in marital satisfaction and affecting factors to enhance couples’ relationships

Application of Nanoparticles as New Therapeutic Methods on Monitoring and Treating Obesity: a Systematic Review

Introduction: Nowadays, obesity has become a major global health challenge and it is closely linked with many metabolic disorders like diabetes and hypertension. Although various types of drugs have been developed, the prevalence of obesity is increasing worldwide. One of the effective way to treat obesity and prevent metabolic disorders coming after, is using nanoparticles as new therapeutic methods.  This study reviewed nanoparticles effects on monitoring and treating obesity. Methods and Results: In this systematic review, based on PRISMA guidelines, two persons independently searched MeSH terms “nanoparticles”, “drugs” and “obesity” and some other relevant terms in databases including PubMed, Medline, Scopus, and Cochrane library up to July 2017, and all the articles with considered inclusion criteria were added to the study. 145 articles were obtained by primary searching. After removing 12 irrelevant and 57 duplicate articles, 76 with inclusion criteria were added to this study. Nanoparticles could be used on both monitoring and treating obesity. These nanoceria when conjugated with antibodies are used for monitoring proteins involve in inflammation and insulin signaling without cell lysis. For example, due to TNF- α inflammatory cytokine that has an important role in insulin resistance in obesity, these nanoceria can be applied on monitoring some proteins like multiple kinases that ameliorate insulin resistance in obesity. Drug delivery via nanoparticles made it more possible to have better effects on regulating lipase cycle in adipose cells and tissues. Furthermore, application of nanoparticles conjugated with some agents can affect intracellular signaling or mechanisms in order to treat obesity. For example, silica nanoparticle has anti-brown adipogenic effect via regulation of p38 phosphorylation which involves in obesity. Conclusions: Application of nanoparticles increases the accessibility of adipose targeting cells. these nanoceria might be used to detect and treat many metabolic disorders like obesity. Producing more efficient and complicated nanoparticles in sizes depended manner, provides more opportunities to access better on targeted cells for treatment, however,  their side effects on normal cells should be considered

Visual Prompting: Modifying Pixel Space to Adapt Pre-trained Models

Prompting has recently become a popular paradigm for adapting language models to downstream tasks. Rather than fine-tuning model parameters or adding task-specific heads, this approach steers a model to perform a new task simply by adding a text prompt to the model's inputs. In this paper, we explore the question: can we create prompts with pixels instead? In other words, can pre-trained vision models be adapted to a new task solely by adding pixels to their inputs? We introduce visual prompting, which learns a task-specific image perturbation such that a frozen pre-trained model prompted with this perturbation performs a new task. We discover that changing only a few pixels is enough to adapt models to new tasks and datasets, and performs on par with linear probing, the current de facto approach to lightweight adaptation. The surprising effectiveness of visual prompting provides a new perspective on how to adapt pre-trained models in vision, and opens up the possibility of adapting models solely through their inputs, which, unlike model parameters or outputs, are typically under an end-user's control. Code is available at http://hjbahng.github.io/visual_prompting .Comment: 17 pages, 10 figure

Paint by Word

We investigate the problem of zero-shot semantic image painting. Instead of painting modifications into an image using only concrete colors or a finite set of semantic concepts, we ask how to create semantic paint based on open full-text descriptions: our goal is to be able to point to a location in a synthesized image and apply an arbitrary new concept such as "rustic" or "opulent" or "happy dog." To do this, our method combines a state-of-the art generative model of realistic images with a state-of-the-art text-image semantic similarity network. We find that, to make large changes, it is important to use non-gradient methods to explore latent space, and it is important to relax the computations of the GAN to target changes to a specific region. We conduct user studies to compare our methods to several baselines.Comment: 10 pages, 9 figure