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We propose a novel framework for hair animation as well as hair-water interaction that supports millions of hairs. First, we develop a hair animation framework that embeds hair into a tetrahedralized volume mesh that we kinematically skin to deform and follow the exterior of an animated character. Allowing the hairs to follow their precomputed embedded locations in the kinematically deforming skinned mesh already provides visually plausible behavior. Creating a copy of the tetrahedral mesh, endowing it with springs, and attaching it to the kinematically skinned mesh creates more dynamic behavior. Notably, the springs can be quite weak and thus efficient to simulate because they are structurally supported by the kinematic mesh. If independent simulation of individual hairs or guide hairs is desired, they too benefit from being anchored to the kinematic mesh dramatically increasing efficiency as weak springs can be used while still supporting interesting and dramatic hairstyles. Furthermore, we explain how to embed these dynamic simulations into the kinematically deforming skinned mesh so that they can be used as part of a blendshape system where an artist can make many subsequent iterations without requiring any additional simulation. Although there are many applications for our newly proposed approach to hair animation, we mostly focus on the particularly challenging problem of hair-water interaction. While doing this, we discuss how porosities are stored in the kinematic mesh, how the kinematically deforming mesh can be used to apply drag and adhesion forces to the water, etc.

Modern-day techniques for designing neural network architectures are highly reliant on trial and error, heuristics, and so-called best practices, without much rigorous justification. After choosing a network architecture, an energy function (or loss) is minimized, choosing from a wide variety of optimization and regularization methods. Given the ad-hoc nature of network architecture design, it would be useful if the optimization led to a sparse solution so that one could ascertain the importance or unimportance of various parts of the network architecture. Of course, historically, sparsity has always been a useful notion for inverse problems where researchers often prefer the L 1 norm over L 2 . Similarly for control, one often includes the control variables in the objective function in order to minimize their efforts. Motivated by the design and training of neural networks, we propose a novel column space search approach that emphasizes the data over the model, as well as a novel iterative Levenberg-Marquardt algorithm that smoothly converges to a regularized SVD as opposed to the abrupt truncation inherent to PCA. In the case of our iterative Levenberg-Marquardt algorithm, it suffices to consider only the linearized subproblem in order to verify our claims. However, the claims we make about our novel column space search approach require examining the impact of the solution method for the linearized subproblem on the fully nonlinear original problem; thus, we consider a complex real-world inverse problem (determining facial expressions from RGB images).

Muscle-based systems have the potential to provide both anatomical accuracy and semantic interpretability as compared to blendshape models; however, a lack of expressivity and differentiability has limited their impact. Thus, we propose modifying a recently developed rather expressive muscle-based system in order to make it fully-differentiable; in fact, our proposed modifications allow this physically robust and anatomically accurate muscle model to conveniently be driven by an underlying blendshape basis. Our formulation is intuitive, natural, as well as monolithically and fully coupled such that one can differentiate the model from end to end, which makes it viable for both optimization and learning-based approaches for a variety of applications. We illustrate this with a number of examples including both shape matching of three-dimensional geometry as as well as the automatic determination of a three-dimensional facial pose from a single two-dimensional RGB image without using markers or depth information.

While much progress has been made in capturing high-quality facial performances using motion capture markers and shape-from-shading, high-end systems typically also rely on rotoscope curves hand-drawn on the image. These curves are subjective and difficult to draw consistently; moreover, ad-hoc procedural methods are required for generating matching rotoscope curves on synthetic renders embedded in the optimization used to determine three-dimensional facial pose and expression. We propose an alternative approach whereby these curves and other keypoints are detected automatically on both the image and the synthetic renders using trained neural networks, eliminating artist subjectivity and the ad-hoc procedures meant to mimic it. More generally, we propose using machine learning networks to implicitly define deep energies which when minimized using classical optimization techniques lead to three-dimensional facial pose and expression estimation.

RASSF2 is a powerful pro-apoptotic K-Ras effector that is that is inactivated in many tumors via promoter methylation and has been shown to function as a tumor suppressor in lung, colorectal, and breast cell lines. RASSF2 belongs to the Ras-association domain family (RASSF) of proteins, which are able to engage in homo/hetero-dimerization and interact with common binding partners. In the context of acute myelogenous leukemia (AML), RASSF2 is exclusively downregulated in t(8;21) AML, suggesting that its repression may be essential for t(8;21) leukemia development.In order to further characterize RASSF2’s tumor suppressive role in a leukemic context, we performed a CRISPR/Cas9-mediated knockout of RASSF2 in two non-t(8;21) AML cell lines: HL-60 and U937, generating single cell isolated clonal lines that are RASSF2 wild type, heterozygous, and knockout. Among the clonal cell lines, we assayed for changes in proliferation, apoptosis, cell cycle, and differentiation. We observed that heterozygous knockdown of RASSF2 in the U937 cells resulted in significantly higher (p

Recently non-coding RNA (ncRNA) genes have been found to serve many important functions in the cell such as regulation of gene expression at the transcriptional level. Potentially there are more ncRNA molecules yet to be found and their possible functions are to be revealed. The discovery of ncRNAs is a difficult task because they lack sequence indicators such as the start and stop codons displayed by protein-coding RNAs. Current methods utilize either sequence motifs or structural parameters to detect novel ncRNAs within genomes. Here, we present an ab initio ncRNA finder, named ncRNAscout, by utilizing both sequence motifs and structural parameters. Specifically, our method has three components: (i) a measure of the frequency of a sequence, (ii) a measure of the structural stability of a sequence contained in a t-score, and (iii) a measure of the frequency of certain patterns within a sequence that may indicate the presence of ncRNA. Experimental results show that, given a genome and a set of known ncRNAs, our method is able to accurately identify and locate a significant number of ncRNA sequences in the genome. The ncRNAscout tool is available for downloading at http://bioinformatics.njit.edu/ncRNAscout.

We present a fast, fully automatic morphing algorithm for creating simulatable flesh and muscle models for human and humanoid faces. Current techniques for creating such models require a significant amount of time and effort, making them infeasible or impractical. In fact, the vast majority of research papers use only a floating mask with no inner lips, teeth, tongue, eyelids, eyes, head, ears, etc.---and even those that build the full visual model would typically still lack the cranium, jaw, muscles, and other internal anatomy. Our method requires only the target surface mesh as input and can create a variety of models in only a few hours with no user interaction. We start with a symmetric, high resolution, anatomically accurate template model that includes auxiliary information such as feature points and curves. Then given a target mesh, we automatically orient it to the template, detect feature points, and use these to bootstrap the detection of corresponding feature curves. These curve correspondences are used to deform the surface mesh of the template model to match the target mesh. Then, the calculated displacements of the template surface mesh are used to drive a three-dimensional morph of the full template model including all interior anatomy. The resulting target model can be simulated to generate a large range of expressions that are consistent across characters using the same muscle activations. Full automation of this entire process makes it readily available to a wide range of users.