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1. Quantum stabilizer codes, lattices, and CFTs

   https://doi.org/10.1007/JHEP03(2021)160  

   Dymarsky, Anatoly; Shapere, Alfred (March 2021, Journal of High Energy Physics)

   null (Ed.)

   A bstract There is a rich connection between classical error-correcting codes, Euclidean lattices, and chiral conformal field theories. Here we show that quantum error-correcting codes, those of the stabilizer type, are related to Lorentzian lattices and non-chiral CFTs. More specifically, real self-dual stabilizer codes can be associated with even self-dual Lorentzian lattices, and thus define Narain CFTs. We dub the resulting theories code CFTs and study their properties. T-duality transformations of a code CFT, at the level of the underlying code, reduce to code equivalences. By means of such equivalences, any stabilizer code can be reduced to a graph code. We can therefore represent code CFTs by graphs. We study code CFTs with small central charge c = n ≤ 12, and find many interesting examples. Among them is a non-chiral E 8 theory, which is based on the root lattice of E 8 understood as an even self-dual Lorentzian lattice. By analyzing all graphs with n ≤ 8 nodes we find many pairs and triples of physically distinct isospectral theories. We also construct numerous modular invariant functions satisfying all the basic properties expected of the CFT partition function, yet which are not partition functions of any known CFTs. We consider the ensemble average over all code theories, calculate the corresponding partition function, and discuss its possible holographic interpretation. The paper is written in a self-contained manner, and includes an extensive pedagogical introduction and many explicit examples. 

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2. DPGen: Automated Program Synthesis for Differential Privacy

   Wang, Yuxin; Ding, Zeyu; Xiao, Yingtai; Kifer, Daniel; Zhang, Danfeng (January 2021, The ACM Conference on Computer and Communications Security (CCS))

   null (Ed.)

   Differential privacy has become a de facto standard for releasing data in a privacy-preserving way. Creating a differentially private algorithm is a process that often starts with a noise-free (nonprivate) algorithm. The designer then decides where to add noise, and how much of it to add. This can be a non-trivial process – if not done carefully, the algorithm might either violate differential privacy or have low utility. In this paper, we present DPGen, a program synthesizer that takes in non-private code (without any noise) and automatically synthesizes its differentially private version (with carefully calibrated noise). Under the hood, DPGen uses novel algorithms to automatically generate a sketch program with candidate locations for noise, and then optimize privacy proof and noise scales simultaneously on the sketch program. Moreover, DPGen can synthesize sophisticated mechanisms that adaptively process queries until a specified privacy budget is exhausted. When evaluated on standard benchmarks, DPGen is able to generate differentially private mechanisms that optimize simple utility functions within 120 seconds. It is also powerful enough to synthesize adaptive privacy mechanisms. 

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3. Optimizing data-intensive computations in existing libraries with split annotations

   https://doi.org/10.1145/3341301.3359652  

   Palkar, Shoumik; Zaharia, Matei (October 2019, SOSP)

   Data movement between main memory and the CPU is a major bottleneck in parallel data-intensive applications. In response, researchers have proposed using compilers and intermediate representations (IRs) that apply optimizations such as loop fusion under existing high-level APIs such as NumPy and TensorFlow. Even though these techniques generally do not require changes to user applications, they require intrusive changes to the library itself: often, library developers must rewrite each function using a new IR. In this paper, we propose a new technique called split annotations (SAs) that enables key data movement optimizations over unmodified library functions. SAs only require developers to annotate functions and implement an API that specifies how to partition data in the library. The annotation and API describe how to enable cross-function data pipelining and parallelization, while respecting each function's correctness constraints. We implement a parallel runtime for SAs in a system called Mozart. We show that Mozart can accelerate workloads in libraries such as Intel MKL and Pandas by up to 15x, with no library modifications. Mozart also provides performance gains competitive with solutions that require rewriting libraries, and can sometimes outperform these systems by up to 2x by leveraging existing hand-optimized code. 

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4. SOAR: A Synthesis Approach for Data Science API Refactoring

   https://doi.org/10.1109/ICSE43902.2021.00023  

   Ni, Ansong; Ramos, Daniel; Yang, Aidan Z.H.; Lynce, Inês; Manquinho, Vasco; Martins, Ruben; Le Goues, Claire (January 2021, 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE))

   With the growth of the open-source data science community, both the number of data science libraries and the number of versions for the same library are increasing rapidly. To match the evolving APIs from those libraries, open-source organizations often have to exert manual effort to refactor the APIs used in the code base. Moreover, due to the abundance of similar open-source libraries, data scientists working on a certain application may have an abundance of libraries to choose, maintain and migrate between. The manual refactoring between APIs is a tedious and error-prone task. Although recent research efforts were made on performing automatic API refactoring between different languages, previous work relies on statistical learning with collected pairwise training data for the API matching and migration. Using large statistical data for refactoring is not ideal because such training data will not be available for a new library or a new version of the same library. We introduce Synthesis for Open-Source API Refactoring (SOAR), a novel technique that requires no training data to achieve API migration and refactoring. SOAR relies only on the documentation that is readily available at the release of the library to learn API representations and mapping between libraries. Using program synthesis, SOAR automatically computes the correct configuration of arguments to the APIs and any glue code required to invoke those APIs. SOAR also uses the interpreter’s error messages when running refactored code to generate logical constraints that can be used to prune the search space. Our empirical evaluation shows that SOAR can successfully refactor 80% of our benchmarks corresponding to deep learning models with up to 44 layers with an average run time of 97.23 seconds, and 90% of the data wrangling benchmarks with an average run time of 17.31 seconds. 

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5. Automata for subregular syntax: Syntax with strings attached

   https://doi.org/10.7275/scil.3184  

   Graf, Thomas; Hanson, Kenneth (January 2025, Proceedings of the Society for Computation in Linguistics)

   Building on recent work in subregular syntax, we argue that syntactic constraints are best understood as operating not over trees, but rather strings that track structural relations such as dominance and c-command. Even constraints that seem intrinsically tied to trees (e.g. constraints on tree tiers) can be reduced to such strings. We define serial constraints as an abstraction that decomposes string constraints into a context function (which associates nodes with strings) and a requirement function (which enforces constraints on these strings). We provide a general procedure for implementing serial constraints as deterministic tree automata. The construction reveals that the many types of constraints found in subregular syntax are variants of the same computational template. Our findings open up a string-based perspective on syntactic constraints and provide a new, very general approach to the automata-theoretic study of subregular complexity. 

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