Fashion Conversation Data on Instagram

The fashion industry is establishing its presence on a number of visual-centric social media like Instagram. This creates an interesting clash as fashion brands that have traditionally practiced highly creative and editorialized image marketing now have to engage with people on the platform that epitomizes impromptu, realtime conversation. What kinds of fashion images do brands and individuals share and what are the types of visual features that attract likes and comments? In this research, we take both quantitative and qualitative approaches to answer these questions. We analyze visual features of fashion posts first via manual tagging and then via training on convolutional neural networks. The classified images were examined across four types of fashion brands: mega couture, small couture, designers, and high street. We find that while product-only images make up the majority of fashion conversation in terms of volume, body snaps and face images that portray fashion items more naturally tend to receive a larger number of likes and comments by the audience. Our findings bring insights into building an automated tool for classifying or generating influential fashion information. We make our novel dataset of {24,752} labeled images on fashion conversations, containing visual and textual cues, available for the research community.Comment: 10 pages, 6 figures, This paper will be presented at ICWSM'1

Solution-Processed Vertically Stacked Complementary Organic Circuits with Inkjet-Printed Routing

The fabrication and measurements of solution‐processed vertically stacked complementary organic field‐effect transistors (FETs) with a high static noise margin (SNM) are reported. In the device structure, a bottom‐gate p‐type organic FET (PFET) is vertically integrated on a top‐gate n‐type organic FET (NFET) with the gate shared in‐between. A new strategy has been proposed to maximize the SNM by matching the driving strengths of the PFET and the NFET by independently adjusting the dielectric capacitance of each type of transistor. Using ideally balanced inverters with the transistor‐on‐transistor structure, the first examples of universal logic gates by inkjet‐printed routing are demonstrated. It is believed that this work can be extended to large‐scale complementary integrated circuits with a high transistor density, simpler routing path, and high yield.1196Ysciescopu

Exosomes from Human Adipose Tissue-Derived Mesenchymal Stem Cells Promote Epidermal Barrier Repair by Inducing de Novo Synthesis of Ceramides in Atopic Dermatitis.

Atopic dermatitis (AD) is a multifactorial, heterogeneous disease associated with epidermal barrier disruption and intense systemic inflammation. Previously, we showed that exosomes derived from human adipose tissue-derived mesenchymal stem cells (ASC-exosomes) attenuate AD-like symptoms by reducing multiple inflammatory cytokine levels. Here, we investigated ASC-exosomes' effects on skin barrier restoration by analyzing protein and lipid contents. We found that subcutaneous injection of ASC-exosomes in an oxazolone-induced dermatitis model remarkably reduced trans-epidermal water loss, while enhancing stratum corneum (SC) hydration and markedly decreasing the levels of inflammatory cytokines such as IL-4, IL-5, IL-13, TNF-α, IFN-γ, IL-17, and TSLP, all in a dose-dependent manner. Interestingly, ASC-exosomes induced the production of ceramides and dihydroceramides. Electron microscopic analysis revealed enhanced epidermal lamellar bodies and formation of lamellar layer at the interface of the SC and stratum granulosum with ASC-exosomes treatment. Deep RNA sequencing analysis of skin lesions demonstrated that ASC-exosomes restores the expression of genes involved in skin barrier, lipid metabolism, cell cycle, and inflammatory response in the diseased area. Collectively, our results suggest that ASC-exosomes effectively restore epidermal barrier functions in AD by facilitating the de novo synthesis of ceramides, resulting in a promising cell-free therapeutic option for treating AD

Modeling Bursty Temporal Pattern of Rumors

Online social networks enable fast and wide diffusion of rumors, which could be potential threats to the society. Understanding the rumor spreading mechanism is therefore essential. This demo presents an agent-pattern based model that can explain the spiky temporal diffusion pattern of rumors. The demo platform provides an interface allowing users to check a wide set of parameters contribute to the characteristic temporal pattern of rumors

Rumor Detection over Varying Time Windows

<div><p>This study determines the major difference between rumors and non-rumors and explores rumor classification performance levels over varying time windows—from the first three days to nearly two months. A comprehensive set of user, structural, linguistic, and temporal features was examined and their relative strength was compared from near-complete date of Twitter. Our contribution is at providing deep insight into the cumulative spreading patterns of rumors over time as well as at tracking the precise changes in predictive powers across rumor features. Statistical analysis finds that structural and temporal features distinguish rumors from non-rumors over a long-term window, yet they are not available during the initial propagation phase. In contrast, user and linguistic features are readily available and act as a good indicator during the initial propagation phase. Based on these findings, we suggest a new rumor classification algorithm that achieves competitive accuracy over both short and long time windows. These findings provide new insights for explaining rumor mechanism theories and for identifying features of early rumor detection.</p></div

Selected features.

<p>Selected features.</p

Fashion Conversation Data on Instagram

The fashion industry is establishing its presence on a number of visual-centric social media like Instagram. This creates an interesting clash as fashion brands that have traditionally practiced highly creative and editorialized image marketing now have to engage with people on the platform that epitomizes impromptu, realtime conversation. What kinds of fashion images do brands and individuals share and what are the types of visual features that attract likes and comments? In this research, we take both quantitative and qualitative approaches to answer these questions. We analyze visual features of fashion posts first via manual tagging and then via training on convolutional neural networks. The classified images were examined across four types of fashion brands: mega couture, small couture, designers, and high street. We find that while product-only images make up the majority of fashion conversation in terms of volume, body snaps and face images that portray fashion items more naturally tend to receive a larger number of likes and comments by the audience. Our findings bring insights into building an automated tool for classifying or generating influential fashion information. We make our novel dataset of 24,752 labeled images on fashion conversations, containing visual and textual cues, available for the research community

Fashion conversation data on Instagram

Our fashion dataset is composed of information about 24,752 posts by 13,350 people on Instagram. The data collection was done over a month period in January, 2015. We searched for posts mentioning 48 internationally renowned fashion brand names as hashtag. Our data contain information about hashtags as well as image features based on deep learning (Convolutional Neural Network or CNN). The list of learned features include selfies, body snaps, marketing shots, non-fashion, faces, logo, etc. Please refer to our paper for the full description of how we built our deep learning model

Samples of extracted time series.

<p>The time series are extracted from 56-day observation period(<i>x</i>-axis = days; <i>y</i>-axis = number of tweets). Rumors typically have longer life spans and more fluctuations.</p