1 SG, 9.2%, 180 fps 3 SGs, 6.0% , 76 fps 5 SGs, 3.4 %, 55 fps 7 SGs, 2.0% , 36 fps 9 SGs, 1.1% , 27 fps 1 ASG 11 SGs, 0.54% , 22 fps 13 SGs, 0.26% , 19 fps 15 SGs, 0.11% , 17 fps 1 ASG, 0.10% , 125 fps reference Figure 1: Comparison of the SG (Spherical Gaussian) based approximation with the ASG (Anisotropic Spherical Gaussian) based approximation in rendering a highly anisotropic metal dish, under an environment light and two local lights. The BRDF of the metal dish is approximated by different number of ASGs or SGs in different images. Notice the superior property of ASGs over SGs. The result generated by 1 ASG already matches the path-traced reference well (with a L 2 error of 0.10%), and achieves a high framerate of 125 fps, while, to achieve a similar quality, more than 10 SGs are required, but with much lower framerates (19 fps for 13 SGs or 17 fps for 15 SGs). The L 2 error and the framerates for each configuration are also given in the corresponding subtitle. AbstractWe present a novel anisotropic Spherical Gaussian (ASG) function, built upon the Bingham distribution [Bingham 1974], which is much more effective and efficient in representing anisotropic spherical functions than Spherical Gaussians (SGs). In addition to retaining many desired properties of SGs, ASGs are also rotationally invariant and capable of representing all-frequency signals. To further strengthen the properties of ASGs, we have derived approximate closed-form solutions for their integral, product and convolution operators, whose errors are nearly negligible, as validated by quantitative analysis. Supported by all these operators, ASGs can be adapted in existing SG-based applications to enhance their scalability in handling anisotropic effects. To demonstrate the accuracy and efficiency of ASGs in practice, we have applied ASGs in two important SG-based rendering applications and the experimental results clearly reveal the merits of ASGs.

We describe an interactive design tool for authoring, simulating, and adjusting yarn-level patterns for knitted and woven cloth. To achieve interactive performance for notoriously slow yarn-level simulations, we propose two acceleration schemes: (a) yarn-level periodic boundary conditions that enable the restricted simulation of only small periodic patches, thereby exploiting the spatial repetition of many cloth patterns in cardinal directions, and (b) a highly parallel GPU solver for efficient yarn-level simulation of the small patch. Our system supports interactive pattern editing and simulation, and runtime modification of parameters. To adjust the amount of material used (yarn take-up) we support "on the fly" modification of (a) local yarn rest-length adjustments for pattern specific edits, e.g., to tighten slip stitches, and (b) global yarn length by way of a novel yarn-radius similarity transformation. We demonstrate the tool's ability to support interactive modeling, by novice users, of a wide variety of yarn-level knit and woven patterns. Finally, to validate our approach, we compare dozens of generated patterns against reference images of actual woven or knitted cloth samples, and we release this corpus of digital patterns and simulated models as a public dataset to support future comparisons.

Figure 1: Our method enables surfaces to be printed as 3D wireframes using arbitrary meshes. This enables improved shape approximation and shape depiction as compared to previous approaches.

Anthracyclines, such as doxorubicin, represent one group of chemotherapy drugs with the most cardiotoxicity. Despite that anthracyclines are capable of treating assorted solid tumors and hematological malignancies, the side effect of inducing cardiac dysfunction has hampered their clinical use. Currently, the mechanism underlying anthracycline cardiotoxicity remains obscure. Increasing evidence points to mitochondria, the energy factory of cardiomyocytes, as a major target of anthracyclines. In this review, we will summarize recent findings about mitochondrial mechanism during anthracycline cardiotoxicity. In particular, we will focus on the following aspects: 1) the traditional view about anthracycline-induced reactive oxygen species (ROS), which is produced by mitochondria, but in turn causes mitochondrial injury. 2) Mitochondrial iron-overload and ferroptosis during anthracycline cardiotoxicity. 3) Autophagy, mitophagy and mitochondrial dynamics during anthracycline cardiotoxicity. 4) Anthracycline-induced disruption of cardiac metabolism.

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