Large-scale Text-to-Image Generation Models for Visual Artists' Creative Works

Large-scale Text-to-image Generation Models (LTGMs) (e.g., DALL-E), self-supervised deep learning models trained on a huge dataset, have demonstrated the capacity for generating high-quality open-domain images from multi-modal input. Although they can even produce anthropomorphized versions of objects and animals, combine irrelevant concepts in reasonable ways, and give variation to any user-provided images, we witnessed such rapid technological advancement left many visual artists disoriented in leveraging LTGMs more actively in their creative works. Our goal in this work is to understand how visual artists would adopt LTGMs to support their creative works. To this end, we conducted an interview study as well as a systematic literature review of 72 system/application papers for a thorough examination. A total of 28 visual artists covering 35 distinct visual art domains acknowledged LTGMs' versatile roles with high usability to support creative works in automating the creation process (i.e., automation), expanding their ideas (i.e., exploration), and facilitating or arbitrating in communication (i.e., mediation). We conclude by providing four design guidelines that future researchers can refer to in making intelligent user interfaces using LTGMs.Comment: 15 pages, 3 figure

Polyploidization Altered Gene Functions in Cotton (Gossypium spp.)

Cotton (Gossypium spp.) is an important crop plant that is widely grown to produce both natural textile fibers and cottonseed oil. Cotton fibers, the economically more important product of the cotton plant, are seed trichomes derived from individual cells of the epidermal layer of the seed coat. It has been known for a long time that large numbers of genes determine the development of cotton fiber, and more recently it has been determined that these genes are distributed across At and Dt subgenomes of tetraploid AD cottons. In the present study, the organization and evolution of the fiber development genes were investigated through the construction of an integrated genetic and physical map of fiber development genes whose functions have been verified and confirmed. A total of 535 cotton fiber development genes, including 103 fiber transcription factors, 259 fiber development genes, and 173 SSR-contained fiber ESTs, were analyzed at the subgenome level. A total of 499 fiber related contigs were selected and assembled. Together these contigs covered about 151 Mb in physical length, or about 6.7% of the tetraploid cotton genome. Among the 499 contigs, 397 were anchored onto individual chromosomes. Results from our studies on the distribution patterns of the fiber development genes and transcription factors between the At and Dt subgenomes showed that more transcription factors were from Dt subgenome than At, whereas more fiber development genes were from At subgenome than Dt. Combining our mapping results with previous reports that more fiber QTLs were mapped in Dt subgenome than At subgenome, the results suggested a new functional hypothesis for tetraploid cotton. After the merging of the two diploid Gossypium genomes, the At subgenome has provided most of the genes for fiber development, because it continues to function similar to its fiber producing diploid A genome ancestor. On the other hand, the Dt subgenome, with its non-fiber producing D genome ancestor, provides more transcription factors that regulate the expression of the fiber genes in the At subgenome. This hypothesis would explain previously published mapping results. At the same time, this integrated map of fiber development genes would provide a framework to clone individual full-length fiber genes, to elucidate the physiological mechanisms of the fiber differentiation, elongation, and maturation, and to systematically study the functional network of these genes that interact during the process of fiber development in the tetraploid cottons

Green and Sustainable Hot Melt Adhesive (HMA) Based on Polyhydroxyalkanoate (PHA) and Silanized Cellulose Nanofibers (SCNFs)

Polyhydroxyalkanoate (PHA), with a long chain length and high poly(4–hydroxybutyric acid) (P4HB) ratio, can be used as a base polymer for eco-friendly and biodegradable adhesives owing to its high elasticity, elongation at break, flexibility, and processability; however, its molecular structures must be adjusted for adhesive applications. In this study, surface-modified cellulose nanofibers (CNFs) were used as a hydrophobic additive for the PHA-based adhesive. For the surface modification of CNFs, double silanization using tetraethyl orthosilicate (TEOS) and methyltrimethoxysilane (MTMS) was performed, and the thermal and structural properties were evaluated. The hydrophobicity of the TEOS- and MTMS-treated CNFs (TMCNFs) was confirmed by FT-IR and water contact angle analysis, with hydrophobic CNFs well dispersed in the PHA. The PHA–CNFs composite was prepared with TMCNFs, and its morphological analysis verified the good dispersion of TMCNFs in the PHA. The tensile strength of the composite was enhanced when 10% TMCNFs were added; however, the viscosity decreased as the TMCNFs acted as a thixotropic agent. Adding TMCNFs to PHA enhanced the flowability and infiltration ability of the PHA–TMCNFs-based adhesive, and an increase in the loss tangent (Tan δ) and adjustment of viscosity without reducing the adhesive strength was also observed. These changes in properties can improve the flowability and dispersibility of the PHA–TMCNFs adhesive on a rough adhesive surface at low stress. Thus, it is expected that double-silanized CNFs effectively improve their interfacial adhesion in PHA and the adhesive properties of the PHA–CNFs composites, which can be utilized for more suitable adhesive applications

Disentangled Representation of Data Distributions in Scatterplots

© 2019 IEEE.We present a data-driven approach to obtain a disentangled and interpretable representation that can characterize bivariate data distributions of scatterplots. We first collect tabular datasets from the Web and build a training corpus consisting of over one million scatterplot images. Then, we train a state-of-the-art disentangling model, β-variational autoencoder, to derive a disentangled representation of the scatterplot images. The main output of this work is a list of 32 representative features that can capture the underlying structures of bivariate data distributions. Through latent traversals, we seek for high-level semantics of the features and compare them to previous human-derived concepts such as scagnostics measures. Finally, using the 32 features as an input, we build a simple neural network to predict the perceptual distances between scatterplots that were previously scored by human annotators. We found Pearson's correlation coefficient between the predicted and perceptual distances was above 0.75, which indicates the effectiveness of our representation in the quantitative characterization of scatterplots.N

GameDepot: A Visual Analytics System for Mobile Game Performance Testing

In this paper, we present GameDepot, a visual analytics system designed to enable interactive analysis of performance testing logs for mobile games. Due to the emergence of computational-heavy mobile games, the performance testing of mobile games has become a crucial practice to strike a balance between user experience, battery life, and thermal management. However, policymakers in device manufacturers face challenges in understanding the complex performance testing logs generated for different game and device combinations. To address this issue, we conduct a design study with ten domain experts and develop GameDepot, a visual analytics system with six tightly coordinated views that provide an overview of thousands of testing sessions and facilitate the identification of abnormal events, such as excessive throttling. We implement a data modeling pipeline based on Long Short Term Memory (LSTM) to support advanced data manipulation operations that are needed in practice, such as measuring the similarity between devices and or predicting the performance of an unseen combination of games and devices. Our evaluation demonstrates that GameDepot not only supports the seven essential tasks we identified during the design study but also facilitates the identification of abnormal sessions

Environmentally Constrained Optimal Dispatch Method for Combined Cooling, Heating, and Power Systems Using Two-Stage Optimization

The reliance on coal-fired power generation has gradually reduced with the growing interest in the environment and safety, and the environmental effects of power generation are now being considered. However, it can be difficult to provide stable power to end-users while minimizing environmental pollution by replacing coal-fired systems with combined cooling, heat, and power (CCHP) systems that use natural gas, because CCHP systems have various power output vulnerabilities. Therefore, purchasing power from external electric grids is essential in areas where CCHP systems are built; hence, optimal CCHP controls should also consider energy purchased from external grids. This study proposes a two-stage algorithm to optimally control CCHP systems. In Stage One, the optimal energy mix using the Lagrange multiplier method for state-wide grids from which CCHP systems purchase deficient electricity was calculated. In Stage Two, the purchased volumes from these grids were used as inputs to the proposed optimization algorithm to optimize CCHP systems suitable for metropolitan areas. We used case studies to identify the accurate energy efficiency, costs, and minimal emissions. We chose the Atlanta area to analyze the CCHP system’s impact on energy efficiency, cost variation, and emission savings. Then, we calculated an energy mix suitable for the region for each simulation period. The case study results confirm that deploying an optimized CCHP system can reduce purchased volumes from the grid while reducing total emissions. We also analyzed the impact of the CCHP system on emissions and cost savings

A Progressive k-d tree for Approximate k-Nearest Neighbors

International audienceWe present a progressive algorithm for approximate k-nearest neighbor search. Although the use of k-nearest neighbor libraries (KNN) is common in many data analysis methods, most KNN algorithms can only be run when the whole dataset has been indexed, i.e., they are not online. Even the few online implementations are not progressive in the sense that the time to index incoming data is not bounded and can exceed the latency required by progressive systems. Exceeding this latency significantly impacts the interactivity of visualization systems especially when dealing with large-scale data. We improve traditional k-d trees for progressive approximate k-nearest neighbor search, enabling fast KNN queries while continuously indexing new batches of data when necessary. Following the progressive computation paradigm, our progressive k-d tree is bounded in time, allowing analysts to access ongoing results within an interactive latency. We also present performance benchmarks to compare online and progressive k-d trees

Drying Effect on Enzymatic Hydrolysis of Cellulose Associated with Porosity and Crystallinity

The effect of drying on the enzymatic hydrolysis of cellulose was determined by analysis of porosity and crystallinity. Fiber hornification induced by drying produced an irreversible reduction in pore volume due to shrinkage and pore collapse, and the decrease in porosity inhibited enzymatic hydrolysis. The drying effect index (DEI) was defined as the difference in enzymatic digestibility between oven- and never-dried pulp, and it was determined that more enzymes caused a higher DEI at the initial stage of enzymatic hydrolysis and the highest DEI was also observed at the earlier stages with higher enzyme dosage. However, there was no significant difference in the DEI with less enzymes because cellulose conversion to sugars during hydrolysis did not enhance enzymatic hydrolysis due to the decrease in enzyme activity. The water retention value (WRV) and Simons’ staining were used to measure pore volume and to investigate the cause of the decrease in enzymatic hydrolysis. A decrease in enzyme accessibility induced by the collapse of enzymes’ accessible larger pores was determined and this decreased the enzymatic hydrolysis. However, drying once did not cause any irreversible change in the crystalline structure, thus it seems there is no correlation between enzymatic digestibility and crystalline structure

PANENE: A Progressive Algorithm for Indexing and Querying Approximate k-Nearest Neighbors

International audienceWe present PANENE, a progressive algorithm for approximate nearest neighbor indexing and querying. Although the use of k-nearest neighbor (KNN) libraries is common in many data analysis methods, most KNN algorithms can only be queried when the whole dataset has been indexed, i.e., they are not online. Even the few online implementations are not progressive in the sense that the time to index incoming data is not bounded and cannot satisfy the latency requirements of progressive systems. This long latency has significantly limited the use of many machine learning methods, such as t-SNE, in interactive visual analytics. PANENE is a novel algorithm for Progressive Approximate k-NEarest NEighbors, enabling fast KNN queries while continuously indexing new batches of data. Following the progressive computation paradigm, PANENE operations can be bounded in time, allowing analysts to access running results within an interactive latency. PANENE can also incrementally build and maintain a cache data structure, a KNN lookup table, to enable constant-time lookups for KNN queries. Finally, we present three progressive applications of PANENE, such as regression, density estimation, and responsive t-SNE, opening up new opportunities to use complex algorithms in interactive systems