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1. Finch: Domain Specific Language and Code Generation for Finite Element and Finite Volume in Julia

   https://doi.org/10.1007/978-3-031-08751-6_9  

   Heisler, Eric; Deshmukh, Aadesh; Sundar, Hari (June 2022, Computational Science -- ICCS 2022)

   We introduce FINCH, a Julia-based domain specific language (DSL) for solving partial differential equations in a discretization agnostic way, currently including finite element and finite volume methods. A key focus is code generation for various internal or external software targets. Internal targets use a modular set of tools in Julia providing a direct solution within the framework. In contrast, external code generation produces a set of code files to be compiled and run with external libraries or frameworks. Examples include a matlab target, for smaller problems or prototyping, or C++/MPI based targets for larger problems needing scalability. This allows us to take advantage of their capabilities without needlessly duplicating them, and provides options tailored to the needs of the domain scientist. The modular design of FINCH allows ongoing development of these target modules resulting in a more extensible framework and a broader set of applications. The support for multiple discretizations, including finite element and finite volume methods, also contributes to this goal. Another focus of this project is complex systems containing a large set of coupled PDEs that could be challenging to efficiently code and optimize by hand, but that are relatively simple to specify using the DSL. In this paper we present the key features of FINCH that set it apart from many other DSL options, and demonstrate the basic usage and current capabilities through examples. 

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2. Top-Down Synthesis for Library Learning

   https://doi.org/10.1145/3571234  

   Bowers, Matthew; Olausson, Theo X.; Wong, Lionel; Grand, Gabriel; Tenenbaum, Joshua B.; Ellis, Kevin; Solar-Lezama, Armando (January 2023, Proceedings of the ACM on Programming Languages)

   This paper introduces corpus-guided top-down synthesis as a mechanism for synthesizing library functions that capture common functionality from a corpus of programs in a domain specific language (DSL). The algorithm builds abstractions directly from initial DSL primitives, using syntactic pattern matching of intermediate abstractions to intelligently prune the search space and guide the algorithm towards abstractions that maximally capture shared structures in the corpus. We present an implementation of the approach in a tool called Stitch and evaluate it against the state-of-the-art deductive library learning algorithm from DreamCoder. Our evaluation shows that Stitch is 3-4 orders of magnitude faster and uses 2 orders of magnitude less memory while maintaining comparable or better library quality (as measured by compressivity). We also demonstrate Stitch’s scalability on corpora containing hundreds of complex programs that are intractable with prior deductive approaches and show empirically that it is robust to terminating the search procedure early—further allowing it to scale to challenging datasets by means of early stopping. 

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3. Erebus: Access Control for Augmented Reality Systems., USENIX Security, 2023. Published.

   Kim, Y.; Goutam, S.; Rahmati, A.; Kaufman, A. (January 2023, USENIX Security)

   Augmented Reality (AR) is widely considered the next evolution in personal devices, enabling seamless integration of the digital world into our reality. Such integration, however, often requires unfettered access to sensor data, causing significant over privilege for applications that run on these platforms. Through analysis of 17 AR systems and 45 popular AR applications, we explore existing mechanisms for access control in AR platforms, identify key trends in how AR applications use sensor data, and pinpoint unique threats users face in AR environments. Using these findings, we design and implement Erebus, an access control framework for AR platforms that enables fine-grained control over data used by AR applications. Erebus achieves the principle of least privileged through the creation of a domain-specific language (DSL) for permission control in AR platforms, allowing applications to specify data needed for their functionality. Using this DSL, Erebus further enables users to customize app permissions to apply under specific user conditions. We implement Erebus on Google’s ARCore SDK and port five existing AR applications to demonstrate the capability of Erebus to secure various classes of apps. Performance results using these applications and various microbenchmarks show that Erebus achieves its security goals while being practical, introducing negligible performance overhead to the AR system. 

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4. SYNAPSE: SYmbolic Neural-Aided Preference Synthesis Engine

   Modak, S; Patton, N; Dillig, I; Biswas, J (January 2025, https://doi.org/10.48550/arXiv.2403.16689)

   This paper addresses the problem of preference learning, which aims to align robot behaviors through learning user specific preferences (e.g. "good pull-over location") from visual demonstrations. Despite its similarity to learning factual concepts (e.g. "red door"), preference learning is a fundamentally harder problem due to its subjective nature and the paucity of person-specific training data. We address this problem using a novel framework called SYNAPSE, which is a neuro-symbolic approach designed to efficiently learn preferential concepts from limited data. SYNAPSE represents preferences as neuro-symbolic programs, facilitating inspection of individual parts for alignment, in a domain-specific language (DSL) that operates over images and leverages a novel combination of visual parsing, large language models, and program synthesis to learn programs representing individual preferences. We perform extensive evaluations on various preferential concepts as well as user case studies demonstrating its ability to align well with dissimilar user preferences. Our method significantly outperforms baselines, especially when it comes to out of distribution generalization. We show the importance of the design choices in the framework through multiple ablation studies. 

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5. A Programming Model for Reliable and Efficient Edge-Based Execution under Resource Variability

   https://doi.org/10.1109/EDGE.2019.00026  

   Song, Zheng; Tilevich, Eli (July 2019, 2019 IEEE International Conference on Edge Computing (EDGE))

   Edge computing applications use the computational, sensor, and networking resources of nearby mobile and stationary computing devices. Because dissimilar devices can provide these resources, one cannot predict which exact combinations of resources will be available at runtime. The resulting variability hinders the development of edge computing applications. To address this problem, we present a new programming model that employs a domain-specific language (DSL), through which the developer declaratively specifies a collection of microservices and how they invoke each other. Given a concise declarative service suite specification, the DSL compiler automatically generates an execution plan, carried out by our distributed runtime. The resulting programming model is both reliable and efficient. The reliability is achieved by enabling the developer to provide equivalent microservices as switch-over recovery strategies. The efficiency is achieved by the DSL compiler orchestrating the speculatively parallel execution of certain equivalent microservices. Our evaluation demonstrates the reliability, efficiency, and expressiveness of the programming model, which can help developers who need to cope with variable resources at the edge. 

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