קולוקוויום וסמינרים

Mesh realization focuses on developing algorithms for fabricating digital curved shapes in the real world. The goal is to derive a framework for realizing 3D shapes using various materials — such as wood, paper, or yarn — while keeping the process as automatic as possible and still allowing the user to make design choices. Material properties (i.e., their possible local deformations) guide the mathematical formulation of the underlying optimization problems.

We focus on two central methods. First, we introduce a technique for computing planar hexagonal meshes, an approach particularly useful in architectural contexts where components must be cut from flat materials. Second, we present an automatic algorithm for generating viable crochet instructions directly from 3D models. We describe the mathematical formulations and optimization strategies, including the constraints and objectives, that are used to achieve the desired results.

Beyond these fabrication-focused approaches, we also develop additional mesh processing techniques needed to support them. These include a general framework for computing regularized geodesic distances and an efficient optimization algorithm capable of handling various regularizers, which can be used to improve shape quality in the realization process. These methods serve as a toolkit for both mesh processing and physical fabrication.

Deep neural networks are known to be susceptible to adversarial perturbations, small perturbations that alter the network's output and exist under strict norm limitations. Universal adversarial perturbations aim to alter the model's output on a set of out-of-sample data and present a more realistic use case, as awareness of the model's exact input is not required. In addition, patch adversarial attacks denote the setting where the adversarial pertubations are limited to consist of patches with a given shape and number. This work studies realistic applications of adversarial attacks on visual-based models and the robustness of inference-based defenses. We first consider a randomized smoothing-based defense and show that adversarial attacks can generalize to distributions of inputs and models. We then optimize physical passive patch universal adversarial attacks on visual odometry-based autonomous navigation systems. A patch adversarial perturbation poses a severe security issue for such navigation systems and can mislead them onto some collision course. Finally, we consider the optimal placement of multiple such patches and, to the best of our knowledge, present the first direct solution to optimizing the locations and pertubations of multiple patches.

As program synthesizers become integrated into IDEs, programmers combine synthesized code and manually written code within the same project. We therefore built a Programming-by-Example (PBE) synthesizer that documents the example specifications provided to it alongside the result snippet that satisfies them.

We also modified the IDE to treat these example scopes as localized tests for the code they surround, in case they or the code are edited. Unless strict limitations are imposed on how users can edit example scopes, and where they can call the synthesizer, scopes of example-specified code can wind up encompassing others.

The programmer can decide to send such a hierarchical scope to the synthesizer, to refactor the code, to automatically correct manually-written code, or simply to fix a mistake found in an example. State of the art PBE synthesizers cannot handle this hierarchical specification: synthesis will only consider the outer-most block, discarding the user intention contained in inner scopes. This can lead to undesired, overfitted programs.

To address this information loss we propose Spec Scooping, a syntax-guided technique to “scoop” out and preserve the intent from inner example scopes. We accompany Spec Scooping with a host of IDE features to help programmers edit example scopes and the code inside them, including an identification of when specifications contradict each other. Since Spec Scooping enriches the specifications sent to the synthesizer, it also requires modifications to the bottom-up Observational Equivalence synthesis algorithm.

We implement Spec Scooping in a tool ScooPy, including a development environment that supports scooping and an extended synthesizer.We evaluate ScooPy on 33 benchmarks based on SyGuS competition benchmarks.

Our results show that hierarchical specifications are generally more expressive, and that, compared to nonhierarchical specifications at the same level of expressiveness, scooping can provide a performance boost. We also performed two small-scale qualitative studies of ScooPy to gauge the benefits of attaching the specifications to a synthesis result and of users’ interaction with ScooPy’s development environment.