Image-to-Image Retrieval by Learning Similarity between Scene Graphs

As a scene graph compactly summarizes the high-level content of an image in a structured and symbolic manner, the similarity between scene graphs of two images reflects the relevance of their contents. Based on this idea, we propose a novel approach for image-to-image retrieval using scene graph similarity measured by graph neural networks. In our approach, graph neural networks are trained to predict the proxy image relevance measure, computed from human-annotated captions using a pre-trained sentence similarity model. We collect and publish the dataset for image relevance measured by human annotators to evaluate retrieval algorithms. The collected dataset shows that our method agrees well with the human perception of image similarity than other competitive baselines.Comment: Accepted to AAAI 202

KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction

The political stance prediction for news articles has been widely studied to mitigate the echo chamber effect -- people fall into their thoughts and reinforce their pre-existing beliefs. The previous works for the political stance problem focus on (1) identifying political factors that could reflect the political stance of a news article and (2) capturing those factors effectively. Despite their empirical successes, they are not sufficiently justified in terms of how effective their identified factors are in the political stance prediction. Motivated by this, in this work, we conduct a user study to investigate important factors in political stance prediction, and observe that the context and tone of a news article (implicit) and external knowledge for real-world entities appearing in the article (explicit) are important in determining its political stance. Based on this observation, we propose a novel knowledge-aware approach to political stance prediction (KHAN), employing (1) hierarchical attention networks (HAN) to learn the relationships among words and sentences in three different levels and (2) knowledge encoding (KE) to incorporate external knowledge for real-world entities into the process of political stance prediction. Also, to take into account the subtle and important difference between opposite political stances, we build two independent political knowledge graphs (KG) (i.e., KG-lib and KG-con) by ourselves and learn to fuse the different political knowledge. Through extensive evaluations on three real-world datasets, we demonstrate the superiority of DASH in terms of (1) accuracy, (2) efficiency, and (3) effectiveness.Comment: 12 pages, 5 figures, 10 tables, the Web Conference 2023 (WWW

Estrogen Regulation of Gene Expression and Analysis of the Role of the Coregulator, Repressor of Estrogen Receptor Activity (Rea)

148 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004.To elucidate the functional activities of REA in diverse estrogen target tissues in vivo, I have used targeted disruption to ablate the REA gene in mice. Genotyping revealed that homozygous animals are not viable, suggesting a crucial role for REA in early development. The viability of heterozygous animals is similar to that of wild type, and female heterozygous animals have an increased body weight relative to age-matched wild-type animals beginning after puberty. Studies in immature heterozygous animals revealed a greater uterine weight gain in response to estradiol (E2) and a greater stimulation of E2 up-regulated genes and a loss of down regulation in genes normally suppressed by E2 in the uterus. Analysis of the histology of the uterus and mammary gland in REA wild type and heterozygous mice revealed gene dosage developmental effects of REA and changes in E2-responsiveness. Studies using mouse embryo fibroblasts (MEFs) revealed that REA heterozygous MEFs displayed a greater transcriptional response to E2. These studies demonstrate that REA is a significant modulator of estrogen responsiveness in vivo.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis

Latent class analysis (LCA) has been applied in many research areas to disentangle the heterogeneity of a population. Despite its popularity, its estimation method is limited to maximum likelihood estimation (MLE), which requires large samples to satisfy both the multivariate normality assumption and local independence assumption. Although many suggestions regarding adequate sample sizes were proposed, researchers continue to apply LCA with relatively smaller samples. When covariates are involved, the estimation issue is encountered more. In this study, we suggest a different estimating approach for LCA with covariates, also known as latent class regression (LCR), using a fuzzy clustering method and generalized structured component analysis (GSCA). This new approach is free from the distributional assumption and stable in estimating parameters. Parallel to the three-step approach used in the MLE-based LCA, we extend an algorithm of fuzzy clusterwise GSCA into LCR. This proposed algorithm has been demonstrated with an empirical data with both categorical and continuous covariates. Because the proposed algorithm can be used for a relatively small sample in LCR without requiring a multivariate normality assumption, the new algorithm is more applicable to social, behavioral, and health sciences

Multi-Component Ni-Ir Electrocatalysts toward Efficient Oxygen Evolution Reaction

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Human-independent activity recognition of construction worker

With recent advancements in sensor and data analysis technology, multiple research on worker activity recognition through wearable sensors have been conducted to solve worker safety and productivity problem at construction sites. However, most rely on pre-trained models which require re-training of each worker to take into account differences between workers. To alleviate this limitation, we propose a human-independent model that can adapt to differences in workers. Our model uses variational-denoising autoencoder with soft parameter sharing to extract common features in different construction activities, achieving 78.64% accuracy which is higher than existing benchmark models.N

Identification of long-standing and emerging agendas in international forest policy discourse

This study examined forest policy agendas developed in international policy-making process by analyzing international forest policy documents from 2001 to 2022 with power, perception, potency, and proximity. The forest policy agendas consistently addressed in the documents were agroforestry, biodiversity, climate change, certification, desertification, deforestation, forest landscape restoration, illegal logging and trade, non-timber forest products, sustainable forest management, traditional knowledge, governance, participation, partnerships, forest tenure, forest fire, forest disease, and community-based forest management. The emerging agendas since 2011 were ecosystem services, reducing emissions from deforestation and forest degradation plus (REDD+), resilience, urban forests, green economy/bioeconomy, and COVID-19. The changes in international forest policy discourse with long-standing and emerging agendas over time showed three characteristics: policy coherence by the power of international environmental conventions; expansion of forest policy targets and areas by perception and proximity of urban forests; and innovative approaches to resilience and bioeconomy by potency and perception. Therefore, this study offers new insights into the creation and transitions of forest policy agendas in the international forest policy discourse

Low-Power Small-Area Inverter-Based DSM for MEMS Microphone

A delta-sigma modulator (DSM) is proposed for the direct connection to micro-electro-mechanical systems (MEMS) microphone. To reduce power, both the DAC reference voltage (VREF) and the DSM supply voltage (VDD) are reduced to 700 mV by limiting the maximum linear acoustic input range to 110 dB SPL (sound pressure level). For the low VDD operation, the switched capacitor (SC) integrators of DSM employ CMOS inverters as amplifiers. A unity-gain buffer compensates the pole error of the SC integrator; it reduces chip area by replacing the auto-zero capacitor of conventional inverter-based SC integrator. Compared to the conventional integrator, the integrator of this brief reduces the pole error from 0.3x0025; to 0.06x0025;, reduces the chip area and the power by 32.4x0025; and 24.8x0025;, respectively. The 3(rd) order DSM in a 65 nm CMOS process was measured to have Walden-figure of merit (FoMw) 89.3fJ/step, dynamic range (DR) 90.1 dB, signal-to-noise ratio (SNR) 87.2 dB, signal-to-noise and distortion ratio (SNDR) 86.4 dB, and power 122 uW at 10 MHz clock frequency (Fs).11Nsciescopu

Which urban agriculture conditions enable or constrain sustainable food production?

Urban agriculture (UA) has been adopted as a strategy for food security in urban areas. This study identified the conditions for development of UA through a systematic review of UA case studies. It classified the enabling and constraining conditions within the three compositional elements of UA – necessity, ability, and opportunity – and determined the primary and secondary conditions for UA design by the country income group. The following conditions are required for both high-income and low/ middle-income countries: Motivation/public awareness; labour/human resources; policy and institutional infrastructure; social capital; and arable land and resources for farming. Agricultural education/training and research and technical development are needed for low and middle-income countries as the key secondary conditions. In high-income countries, a lack of farmers’ knowledge and urban development are the main challenges to UA implementation. Therefore, the research findings could be meaningful evidence for making decisions and designing UA policies for sustainable food production