Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain

It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation

Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain

It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation

Dual Attention GANs for Semantic Image Synthesis

In this paper, we focus on the semantic image synthesis task that aims at transferring semantic label maps to photo-realistic images. Existing methods lack effective semantic constraints to preserve the semantic information and ignore the structural correlations in both spatial and channel dimensions, leading to unsatisfactory blurry and artifact-prone results. To address these limitations, we propose a novel Dual Attention GAN (DAGAN) to synthesize photo-realistic and semantically-consistent images with fine details from the input layouts without imposing extra training overhead or modifying the network architectures of existing methods. We also propose two novel modules, i.e., position-wise Spatial Attention Module (SAM) and scale-wise Channel Attention Module (CAM), to capture semantic structure attention in spatial and channel dimensions, respectively. Specifically, SAM selectively correlates the pixels at each position by a spatial attention map, leading to pixels with the same semantic label being related to each other regardless of their spatial distances. Meanwhile, CAM selectively emphasizes the scale-wise features at each channel by a channel attention map, which integrates associated features among all channel maps regardless of their scales. We finally sum the outputs of SAM and CAM to further improve feature representation. Extensive experiments on four challenging datasets show that DAGAN achieves remarkably better results than state-of-the-art methods, while using fewer model parameters. The source code and trained models are available at https://github.com/Ha0Tang/DAGAN.Comment: Accepted to ACM MM 2020, camera ready (9 pages) + supplementary (10 pages

Effects of Odd–Even Side Chain Length of Alkyl-Substituted Diphenylbithiophenes on First Monolayer Thin Film Packing Structure

Because of their preferential two-dimensional layer-by-layer growth in thin films, 5,5′bis(4-alkylphenyl)-2,2′-bithiophenes (P2TPs) are model compounds for studying the effects of systematic chemical structure variations on thin-film structure and morphology, which in turn, impact the charge transport in organic field-effect transistors. For the first time, we observed, by grazing incidence X-ray diffraction (GIXD), a strong change in molecular tilt angle in a monolayer of P2TP, depending on whether the alkyl chain on the P2TP molecules was of odd or even length. The monolayers were deposited on densely packed ultrasmooth self-assembled alkane silane modified $SiO_2$ surfaces. Our work shows that a subtle change in molecular structure can have a significant impact on the molecular packing structure in thin film, which in turn, will have a strong impact on charge transport of organic semiconductors. This was verified by quantum-chemical calculations that predict a corresponding odd–even effect in the strength of the intermolecular electronic coupling.Chemistry and Chemical BiologyEngineering and Applied Science

Mutant U2AF1-induced alternative splicing of H2afy (macroH2A1) regulates B-lymphopoiesis in mice

Somatic mutations in spliceosome genes are found in ∼50% of patients with myelodysplastic syndromes (MDS), a myeloid malignancy associated with low blood counts. Expression of the mutant splicing factor U2AF1(S34F) alters hematopoiesis and mRNA splicing in mice. Our understanding of the functionally relevant alternatively spliced target genes that cause hematopoietic phenotypes in vivo remains incomplete. Here, we demonstrate that reduced expression of H2afy1.1, an alternatively spliced isoform of the histone H2A variant gene H2afy, is responsible for reduced B cells in U2AF1(S34F) mice. Deletion of H2afy or expression of U2AF1(S34F) reduces expression of Ebf1 (early B cell factor 1), a key transcription factor for B cell development, and mechanistically, H2AFY is enriched at the EBF1 promoter. Induced expression of H2AFY1.1 in U2AF1(S34F) cells rescues reduced EBF1 expression and B cells numbers in vivo. Collectively, our data implicate alternative splicing of H2AFY as a contributor to lymphopenia induced by U2AF1(S34F) in mice and MDS

Homeostatic interferon-lambda response to bacterial microbiota stimulates preemptive antiviral defense within discrete pockets of intestinal epithelium

Interferon-lambda (IFN-λ) protects intestinal epithelial cells (IECs) from enteric viruses by inducing expression of antiviral IFN-stimulated genes (ISGs). Here, we find that bacterial microbiota stimulate a homeostatic ISG signature in the intestine of specific pathogen-free mice. This homeostatic ISG expression is restricted to IECs, depends on IEC-intrinsic expression of IFN-λ receptor

Consequence of aging at Au/HTM/perovskite interface in triple cation 3D and 2D/3D hybrid perovskite solar cells

Perovskite solar cells (PSCs) expressed great potentials for offering a feasible alternative to conventional photovoltaic technologies. 2D/3D hybrid PSCs, where a 2D capping layer is used over the 3D film to avoid the instability issues associated with perovskite film, have been reported with improved stabilities and high power conversion efficiencies (PCE). However, the profound analysis of the PSCs with prolonged operational lifetime still needs to be described further. Heading towards efficient and long-life PSCs, in-depth insight into the complicated degradation processes and charge dynamics occurring at PSCs' interfaces is vital. In particular, the Au/HTM/perovskite interface got a substantial consideration due to the quest for better charge transfer; and this interface is debatably the trickiest to explain and analyze. In this study, multiple characterization techniques were put together to understand thoroughly the processes that occur at the Au/HTM/perovskite interface. Inquest analysis using current–voltage (I–V), electric field induced second harmonic generation (EFISHG), and impedance spectroscopy (IS) was performed. These techniques showed that the degradation at the Au/HTM/perovskite interface significantly contribute to the increase of charge accumulation and change in impedance value of the PSCs, hence resulting in efficiency fading. The 3D and 2D/3D hybrid cells, with PCEs of 18.87% and 20.21%, respectively, were used in this study, and the analysis was performed over the aging time of 5000 h. Our findings propose that the Au/HTM/perovskite interface engineering is exclusively essential for attaining a reliable performance of the PSCs and provides a new perspective towards the stability enhancement for the perovskite-based future emerging photovoltaic technology.Scopu

Enhancing Perceived Safety in Human–Robot Collaborative Construction Using Immersive Virtual Environments

Advances in robotics now permit humans to work collaboratively with robots. However, humans often feel unsafe working alongside robots. Our knowledge of how to help humans overcome this issue is limited by two challenges. One, it is difficult, expensive and time-consuming to prototype robots and set up various work situations needed to conduct studies in this area. Two, we lack strong theoretical models to predict and explain perceived safety and its influence on human–robot work collaboration (HRWC). To address these issues, we introduce the Robot Acceptance Safety Model (RASM) and employ immersive virtual environments (IVEs) to examine perceived safety of working on tasks alongside a robot. Results from a between-subjects experiment done in an IVE show that separation of work areas between robots and humans increases perceived safety by promoting team identification and trust in the robot. In addition, the more participants felt it was safe to work with the robot, the more willing they were to work alongside the robot in the future.University of Michigan Mcubed Grant: Virtual Prototyping of Human-Robot Collaboration in Unstructured Construction EnvironmentsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145620/1/You et al. forthcoming in AutCon.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145620/4/You et al. 2018.pdfDescription of You et al. 2018.pdf : Published Versio

Overexpression of defense response genes in transgenic wheat enhances resistance to Fusarium head blight

Fusarium head blight (FHB) of wheat, caused by Fusarium graminearum and other Fusarium species, is a major disease problem for wheat production worldwide. To combat this problem, large-scale breeding efforts have been established. Although progress has been made through standard breeding approaches, the level of resistance attained is insufficient to withstand epidemic conditions. Genetic engineering provides an alternative approach to enhance the level of resistance. Many defense response genes are induced in wheat during F. graminearum infection and may play a role in reducing FHB. The objectives of this study were (1) to develop transgenic wheat overexpressing the defense response genes α-1-purothionin, thaumatin-like protein 1 (tlp-1), and β-1,3-glucanase; and (2) to test the resultant transgenic wheat lines against F. graminearum infection under greenhouse and field conditions. Using the wheat cultivar Bobwhite, we developed one, two, and four lines carrying the α-1-purothionin, tlp-1, and β-1,3-glucanase transgenes, respectively, that had statistically significant reductions in FHB severity in greenhouse evaluations. We tested these seven transgenic lines under field conditions for percent FHB disease severity, deoxynivalenol (DON) mycotoxin accumulation, and percent visually scabby kernels (VSK). Six of the seven lines differed from the nontransgenic parental Bobwhite line for at least one of the disease traits. A β-1,3-glucanase transgenic line had enhanced resistance, showing lower FHB severity, DON concentration, and percent VSK compared to Bobwhite. Taken together, the results showed that overexpression of defense response genes in wheat could enhance the FHB resistance in both greenhouse and field conditions

A Multi-Sample Based Method for Identifying Common CNVs in Normal Human Genomic Structure Using High-Resolution aCGH Data

BACKGROUND: It is difficult to identify copy number variations (CNV) in normal human genomic data due to noise and non-linear relationships between different genomic regions and signal intensity. A high-resolution array comparative genomic hybridization (aCGH) containing 42 million probes, which is very large compared to previous arrays, was recently published. Most existing CNV detection algorithms do not work well because of noise associated with the large amount of input data and because most of the current methods were not designed to analyze normal human samples. Normal human genome analysis often requires a joint approach across multiple samples. However, the majority of existing methods can only identify CNVs from a single sample. METHODOLOGY AND PRINCIPAL FINDINGS: We developed a multi-sample-based genomic variations detector (MGVD) that uses segmentation to identify common breakpoints across multiple samples and a k-means-based clustering strategy. Unlike previous methods, MGVD simultaneously considers multiple samples with different genomic intensities and identifies CNVs and CNV zones (CNVZs); CNVZ is a more precise measure of the location of a genomic variant than the CNV region (CNVR). CONCLUSIONS AND SIGNIFICANCE: We designed a specialized algorithm to detect common CNVs from extremely high-resolution multi-sample aCGH data. MGVD showed high sensitivity and a low false discovery rate for a simulated data set, and outperformed most current methods when real, high-resolution HapMap datasets were analyzed. MGVD also had the fastest runtime compared to the other algorithms evaluated when actual, high-resolution aCGH data were analyzed. The CNVZs identified by MGVD can be used in association studies for revealing relationships between phenotypes and genomic aberrations. Our algorithm was developed with standard C++ and is available in Linux and MS Windows format in the STL library. It is freely available at: http://embio.yonsei.ac.kr/~Park/mgvd.php