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The tumour suppressor p53 has been implicated in the host defence system against hepatitis C virus (HCV) infection, although the detailed mechanism remains unknown. Here, we found that p53 inhibits HCV replication by downregulating HCV Core protein levels in human hepatoma cells. For this effect, p53 potentiated the role of E6-associated protein (E6AP) as an E3 ligase to induce ubiquitination and proteasomal degradation of HCV Core. Specifically, p53 facilitated the binding of E6AP to HCV Core through direct interactions with the two proteins. In addition, E6AP failed to induce ubiquitination of HCV Core in the absence of p53, suggesting that p53 increases the E3 ligase activity of E6AP in a triple complex consisting of p53, E6AP and HCV Core.

Impostor syndrome is a psychological pattern characterized by doubts in one’s capabilities or achievements despite evident success. In this paper, we demonstrate the potential of a smartphone-aided solution for impostor syndrome detection among high-achieving college students by presenting some initial findings from our 3-week user study. We collected GPS, Bluetooth, light, screen and app usage data along with Clance Impostor Phenomenon Scale (CIPS) survey results from 37 students. We conducted a correlation analysis between CIPS scores and features extracted from sensor data. Then we grouped the features into three categories that reflect the behavioral characteristics of individuals with impostor syndrome, namely social avoidance, sense of obligation, and restlessness. Based on these findings, we suggest further examination of the use of behavioral sensing data in detecting impostor syndrome.

This study suggests a method to select core data that will be helpful for machine learning. Specifically, we form a two-dimensional distribution based on the similarity of the training data and compose grids with fixed ratios on the distribution. In each grid, we select data based on the distribution consistency (DC) of the target class data and examine how it affects the classifier. We use CIFAR-10 for the experiment and set various grid ratios from 0.5 to 0.005. The influences of these variables were analyzed with the use of different training data sizes selected based on high-DC, low-DC (inverse of high DC), and random (no criteria) selections. As a result, the average point accuracy at 0.95% (±0.65) and the point accuracy at 1.54% (±0.59) improved for the grid configurations of 0.008 and 0.005, respectively. These outcomes justify an improved performance compared with that of the existing approach (data distribution search). In this study, we confirmed that the learning performance improved when the training data were selected for very small grid and high-DC settings.

Machine learning is a widely adopted solution to complex and non-linear problems, but it takes considerable labor and time to develop an optimal model with high reliability. The costs increase even more as the model deepens and training data grows. This paper presents a method in which, a technique known as dataset distillation, can be implemented in data selection to reduce the training time. We first train the model with distilled images, and then, predict original train data to measure training contribution as sampling weight of selection. Our method enables the fast and easy calculation of weights even in the case of redesigning a network.

This paper contains a strategy to select training data which affects the accuracy of AI positively. When a machine learning (ML) model does not attain a targeted performance, a basic solution for this is to add more data to the model. In this case, we suggest the criteria for selecting more useful data for the learning result instead of adding data randomly. We define a method, data distribution search (DDS), of selecting evenly across all regions based on the distribution of data. In the experiment using MNIST and CIFAR-10, we could confirm that the data set selected by the DDS was superior to a randomly selected set. Ultimately, we could get that there is a data selection method that affects AI performance positively.

This article presents a real-time bokeh rendering technique that splats pre-computed sprites but takes dynamic visibilities and intrinsic appearances into account at runtime. To attain alias-free looks without excessive sampling on a lens, the visibilities of strong highlights are densely sampled using rasterization, while regular objects are sparsely sampled using conventional defocus-blur rendering. The intrinsic appearance is dynamically transformed from a precomputed look-up table, which encodes radial aberrations against image distances in a compact 2D texture. Our solution can render complex bokeh effects without undersampling artifacts in real time, and greatly improve the photorealism of defocus-blur rendering.

This paper presents a scalable parser framework using graphics processing units (GPUs) for massive text-based files. Specifically, our solution is designed to efficiently parse Wavefront OBJ models texts of which specify 3D geometries and their topology. Our work bases its scalability and efficiency on chunk-based processing. The entire parsing problem is subdivided into subproblems the chunk of which can be processed independently and merged seamlessly. The within-chunk processing is made highly parallel, leveraged by GPUs. Our approach thereby overcomes the bottlenecks of the existing OBJ parsers. Experiments performed to assess the performance of our system showed that our solutions significantly outperform the existing CPU-based solutions and GPU-based solutions as well.

While terrestrial organisms such as livestock are consumed regularly, studies of internal distribution and bioaccumulation of persistent organic pollutants (POPs) have been focused more on aquatic organisms. In this study, we have assessed the internal distribution and fate of legacy (PCDD/Fs and PCBs) and emerging POPs (HBCDs and PFASs), and TBBPA in 42 tissues of a Bos Taurus. PCDD/Fs, DL-PCBs, and HBCDs were found 3, 4, and 4-fold higher in the lipid-rich organs (subcutaneous fat, visceral fat, large intestine) compared to the remaining organs and muscles, owing to their hydrophobic properties. The TBBPA concentration in the excrement was 36-fold higher compared to the average tissues, suggesting a short internal half-life of TBBPA. Among PFASs, PFUnDA displayed 98% contribution from all ionic PFASs in the tissues due to its strong binding affinity, high exposure via feed and water, and increasing emergence of PFUnDA and its precursors in the Southeast Asian countries. While our study suggests that, at the moment, there is no significant health risks to the general Korean population, the future changes in environmental exposure as well as the internal dynamics and fate of various POPs species should be kept in mind when consuming various parts of livestock.

This article presents a novel parametric model to include expressive chromatic aberrations in defocus blur rendering and its effective implementation using the accumulation buffering. Our model modifies the thin-lens model to adopt the axial and lateral chromatic aberrations, which allows us to easily extend them with nonlinear and artistic appearances beyond physical limits. For the dispersion to be continuous, we employ a novel unified 3D sampling scheme, involving both the lens and spectrum. We further propose a spectral equalizer to emphasize particular dispersion ranges. As a consequence, our approach enables more intuitive and explicit control of chromatic aberrations, unlike the previous physically-based rendering methods.

Internet of things recently emerges as a common platform and service for consumer electronics. This paper presents an interactive framework of visualizing and authoring IoT in indoor environments such as home or small office. Building blocks of the framework are virtual sensors and actuators that abstract physical things and their virtual behaviors on top of their physical networks. Their behaviors are abstracted and programmed through visual authoring tools on the web, which allows a casual consumer to easily monitor and define their behaviors even without knowing the underlying physical connections. The user study performed to assess the usability of the visual authoring showed that the visual authoring is easy to use, understandable, and also preferred to typical text-based script programming.

This paper presents a GPU-based rendering algorithm for real-time defocus blur effects, which significantly improves the accumulation buffering. The algorithm combines three distinctive techniques: 1 adaptive discrete geometric level of detail LOD, made popping-free by blending visibility samples across the two adjacent geometric levels; 2 adaptive visibility/shading sampling via sample reuse; 3 visibility supersampling via height-field ray casting. All the three techniques are seamlessly integrated to lower the rendering cost of smooth defocus blur with high visibility sampling rates, while maintaining most of the quality of brute-force accumulation buffering.

Tessellation shaders, available from the latest graphics processing units (GPUs), are increasingly important in high-fidelity real-time rendering. The tessellation shaders allow us to subdivide a coarse input patch to tiny primitives with subdivision schemes such as Loop subdivision [Loop 1987], Catmull-Clark subdivision [Catmull and Clark 1978], and Phong tessellation [Boubekeur and Alexa 2008]. The tessellated geometries represent smoother surfaces, but also their details can be enhanced with displacement mapping for on-the-fly surface elevation. Each output vertex is relocated based on the height information, which refines the geometric details such as peaks, craters, terrain, and wrinkles.

Depth of field (DOF) refers to a distance range in which photographic capture yields acceptable sharp imagery. The DOF effect is crucial in improving the perceptual realism of synthetic images and drawing user's attention. In graphics, object-based approaches served as reference [Cook et al. 1984; Haeberli and Akeley 1990]. However, their low performance, resulting from the repeated rendering for different lens samples, has impeded their real-time use.