Frame semantics is a linguistic theory that has been instantiated for English in the FrameNet lexicon. We solve the problem of frame-semantic parsing using a two-stage statistical model that takes lexical targets (i.e., content words and phrases) in their sentential contexts and predicts frame-semantic structures. Given a target in context, the first stage disambiguates it to a semantic frame. This model uses latent variables and semi-supervised learning to improve frame disambiguation for targets unseen at training time. The second stage finds the target's locally expressed semantic arguments. At inference time, a fast exact dual decomposition algorithm collectively predicts all the arguments of a frame at once in order to respect declaratively stated linguistic constraints, resulting in qualitatively better structures than naïve local predictors. Both components are feature-based and discriminatively trained on a small set of annotated frame-semantic parses. On the SemEval 2007 benchmark data set, the approach, along with a heuristic identifier of frame-evoking targets, outperforms the prior state of the art by significant margins. Additionally, we present experiments on the much larger FrameNet 1.5 data set. We have released our frame-semantic parser as open-source software.

a) Model (b) Coarsening (c) Measurement (d) Simulation (e) Fabrication Figure 1: We introduce measurement-based, dynamics-aware coarsening (DAC) and the Boundary Balanced Impact (BBI) model -accelerating the simulation of dynamic elastica to obtain predictive and efficient accuracy required for fabrication-design testing and validation. We begin with initial models and real world fabricated materials (a). We then apply dynamics-aware, measurement-based coarsening (DAC) (b and c). The DAC model with BBI then simulates a range of designs that match the real-world dynamic behaviors of corresponding 3D-printed objects undergoing large-deformation loading, frictional contact and high-speed, transient dynamics with impact (e).

We report the first fully automatic method for discovering microstructure families with extremal physical properties.

Force-displacement Force histogramPhysically based simulation Stiff Soft Figure 1: 3D printing allows us to print objects with varying deformation properties. The question that we want to answer is: Given a set of printing materials and a 3D object with desired elasticity properties, which material should be used to print the object? For example, given sample ducks (left) with desired elasticity properties (e.g., measured), our system considers several candidate materials that can be used for replicating the ducks (right), and chooses materials that will best match compliance properties when examined by an observer (red and green outlines). Moreover, we can sort all possible materials by their perceived compliance as predicted by our model. The measured compliance is indicated with colors ranging from stiff (blue) to soft (red). AbstractEveryone, from a shopper buying shoes to a doctor palpating a growth, uses their sense of touch to learn about the world. 3D printing is a powerful technology because it gives us the ability to control the haptic impression an object creates. This is critical for both replicating existing, real-world constructs and designing novel ones. However, each 3D printer has different capabilities and supports different materials, leaving us to ask: How can we best replicate a given haptic result on a particular output device? In this work, we address the problem of mapping a real-world material to its nearest 3D printable counterpart by constructing a perceptual model for the compliance of nonlinearly elastic objects. We begin by building a perceptual space from experimentally obtained user comparisons of twelve 3D-printed metamaterials. By comparing this space to a number of hypothetical computational models, we identify those that can be used to accurately and efficiently evaluate human-perceived differences in nonlinear stiffness. Furthermore, we demonstrate how such models can be applied to complex geometries in an interaction-aware way where the compliance is influenced not only by the material properties from which the object is made but also its geometry. We demonstrate several applications of our method in the context of fabrication and evaluate them in a series of user experiments.

Fig. 1. Our two-scale topology optimization framework allows to optimize continuous material properties mapping to printable microstructures (le ) to fabricate high-resolution functional objects (middle) and minimum compliant structures (right).In this paper we present a novel two-scale framework to optimize the structure and the material distribution of an object given its functional specications. Our approach utilizes multi-material microstructures as low-level building blocks of the object. We start by precomputing the material property gamut -the set of bulk material properties that can be achieved with all material microstructures of a given size. We represent the boundary of this material property gamut using a level set eld. Next, we propose an e cient and general topology optimization algorithm that simultaneously computes an optimal object topology and spatially-varying material properties constrained by the precomputed gamut. Finally, we map the optimal spatially-varying material properties onto the microstructures with the corresponding properties in order to generate a high-resolution printable structure. We demonstrate the e cacy of our framework by designing, optimizing, and fabricating objects in di erent material property spaces on the level of a trillion voxels, i.e several orders of magnitude higher than what can be achieved with current systems.

Fig. 1. Our two-scale topology optimization framework allows to optimize continuous material properties mapping to printable microstructures (le ) to fabricate high-resolution functional objects (middle) and minimum compliant structures (right).In this paper we present a novel two-scale framework to optimize the structure and the material distribution of an object given its functional specications. Our approach utilizes multi-material microstructures as low-level building blocks of the object. We start by precomputing the material property gamut -the set of bulk material properties that can be achieved with all material microstructures of a given size. We represent the boundary of this material property gamut using a level set eld. Next, we propose an e cient and general topology optimization algorithm that simultaneously computes an optimal object topology and spatially-varying material properties constrained by the precomputed gamut. Finally, we map the optimal spatially-varying material properties onto the microstructures with the corresponding properties in order to generate a high-resolution printable structure. We demonstrate the e cacy of our framework by designing, optimizing, and fabricating objects in di erent material property spaces on the level of a trillion voxels, i.e several orders of magnitude higher than what can be achieved with current systems.

Figure 1: Examples produced by our data-driven finite element method. Left: A bar with heterogeneous material arrangement is simulated 15x faster than its high-resolution counterpart. Left-Center: Our fast coarsening algorithm dramatically accelerates designing this shoe sole (up to 43x). Right-Center: A comparison to 3D printed results. Right: We repair a flimsy bridge by adding a supporting arch (8.1x) speed-up. We show a High-Res Simulation for comparison. AbstractCrafting the behavior of a deformable object is difficult-whether it is a biomechanically accurate character model or a new multimaterial 3D printable design. Getting it right requires constant iteration, performed either manually or driven by an automated system. Unfortunately, previous algorithms for accelerating three-dimensional finite element analysis of elastic objects suffer from expensive precomputation stages that rely on a priori knowledge of the object's geometry and material composition. In this paper we introduce Data-Driven Finite Elements as a solution to this problem. Given a material palette, our method constructs a metamaterial library which is reusable for subsequent simulations, regardless of object geometry and/or material composition. At runtime, we perform fast coarsening of a simulation mesh using a simple table lookup to select the appropriate metamaterial model for the coarsened elements. When the object's material distribution or geometry changes, we do not need to update the metamaterial library-we simply need to update the metamaterial assignments to the coarsened elements. An important advantage of our approach is that it is applicable to non-linear material models. This is important for designing objects that undergo finite deformation (such as those produced by multimaterial 3D printing). Our method yields speed gains of up to two orders of magnitude while maintaining good accuracy. We demonstrate the effectiveness of the method on both virtual and 3D printed examples in order to show its utility as a tool for deformable object design.

Figure 1: Our method takes an input mesh of primitives (left) and deforms it to a small number of polygons (center). The reduced mesh can be fabricated using a laser cutter and textured to easily create large physical models (right). AbstractWe present a method for converting computer 3D models into physical equivalents. More specifically, we address the problem of approximating a 3D textured mesh using a small number of planar polygonal primitives that form a closed surface. This simplified representation allows us to easily manufacture individual components using computer controlled cutters (e.g., laser cutters or CNC machines). These polygonal pieces can be assembled into the final 3D model using internal planar connectors that are manufactured simultaneously. Our shape approximation algorithm iteratively assigns mesh faces to planar segments and slowly deforms these faces towards corresponding segments. This approach ensures that the output for a given closed mesh is still a closed mesh and avoids introducing self-intersections. After this step we also compute the shape of polygonal connectors that internally hold the whole mesh surface. Both the polygonal surface elements and connectors can be manufactured in a single cutting pass. We validate the use of our method by computing and manufacturing a variety of textured polyhedral models.