bluuebunny/crossref_metadata_2025 huggingface dataset:
Contemporary characteristics, outcomes and novel risk score for Takotsubo cardiomyopathy: a national inpatient sample analysis
Authors: Agrawal Ankit , Bhagat Umesh , Yesilyaprak Abdullah , Bayat Aqieda , Sawhney Aanchal , Arockiam Aro Daniela , Haroun Elio , Faulx Michael , Desai Milind Y , Jaber Wael , Menon Venu , Griffin Brian , Wang Tom Kai Ming
Published: October 2024

Background

Takotsubo cardiomyopathy (TC) is an established differential diagnosis of myocardial infarction with non-obstructive coronaries with significant interest but limited data on prognostication. We reviewed the characteristics and in-hospital outcomes and developed a novel risk score for TC.

Methods

Using the National Inpatient Sample data from 2016 to 2020, we identified adult patients (≥18 years) with acute coronary syndrome (ACS) and TC. We divided the cohort into ACS with and without TC and retrieved baseline data. Multivariable regression analysis was conducted to identify factors associated with TC diagnosis and adverse outcomes, leading to the development of a risk-scoring system.

Results

Among 7 219 004 adult ACS admissions, 78 214 (1.0%) were diagnosed with TC, with a mean age of 68.2 years, 64 526 (82.5%) being female and 5475 (7.0%, compared with 8.4% for other ACS) in-hospital mortality events. Factors significantly associated with TC were female sex (OR 6.78 (95% CI 6.47 to 7.09), p<0.001) and chronic heart failure (OR 1.60 (95% CI 1.54 to 1.66), p<0.001). A novel risk score was developed, including the following parameters: male sex, age >70 years, non-white race, hypertension, hyperlipidemia, history of coronary artery bypass grafting, history of percutaneous coronary intervention, cardiac arrhythmias, renal failure, cardiogenic shock and vasopressor use. The area under curves for in-hospital mortality was 0.716 in the derivation and 0.725 in the validation cohorts.

Conclusions

TC remains a high-risk diagnosis in a minority of ACS cases, with mortality rates similar to other ACS causes. Our novel risk score offers a valuable tool for risk stratification in patients with TC, but external validation is needed to confirm its utility.
Hidden influence? Unmasking conflicts of interest from randomized clinical trials on spinal cord stimulation for chronic pain
Authors: D'Souza Ryan S , Klasova Johana , Kleppel Donald J , Prokop Larry , Hussain Nasir
Published: October 2024

Background

Randomized clinical trials (RCTs) are considered the gold standard for evaluating the efficacy of healthcare interventions. However, conflicts of interest (COIs) can compromise the scientific integrity in these trials. This study characterized COIs in RCTs on spinal cord stimulation for chronic pain, focusing on the prevalence, disclosure, and monetary value of COIs.

Methods

This cross-sectional study analyzed RCTs published from January 1, 2013 to July 27, 2023. Primary outcomes included the presence, disclosure, and monetary value of COIs, while secondary outcomes assessed the presence of direct/indirect COIs, sponsor access to data, and associations between COIs and select variables, including journal impact factor, publication year, and study outcomes.

Results

Of 38 RCTs, 30 (78.9%) reported COIs. On average, 35.6% of authors per RCT had at least one COI, with a mean of 0.7 COIs per author. The mean annual monetary value of COIs was US$41,157.83 per author per RCT. 29 RCTs (76.3%) had undisclosed COIs, with an average of 24.2% of authors per RCT having undisclosed COIs. Sponsor access to data was reported in 67.6% of RCTs. No associations were observed between the mean percentage of authors with COIs and the monetary value of COIs and select dependent variables (impact factor, publication year, and study outcomes).

Conclusions

A substantial majority of RCTs reported COIs with many authors having undisclosed conflicts, highlighting the need for stringent COI disclosure guidelines to maintain research integrity. Expanding COI registry systems globally and increasing non-industry funding are crucial steps toward enhancing transparency and reducing biases in medical research.
Methotrexate treatment hampers induction of vaccine-specific CD4 T cell responses in patients with IMID
Authors: Kummer Laura Y L , Fernández Blanco Laura , Kreher Christine , Bos Amélie , Kuijper Lisan H , Verstegen Niels J M , van de Sandt Carolien E , Konijn Veronique A L , Duurland Mariël C , Menage Charlotte , Jorritsma Tineke , Steenhuis Maurice , Hagen Ruth R , van den Dijssel Jet , de Jongh Rivka , Ashhurst Tom , van Gils Marit J , Garcia-Vallejo Juan J , Claireaux Mathieu , Stalman Eileen W , van Dam Koos P J , Wieske Luuk , Boekel Laura , Wolbink Gertjan , Tas Sander W , Rispens Theo , Kuijpers Taco W , Eftimov Filip , van Ham Sija Marieke , ten Brinke Anja
Published: October 2024

Objectives

Methotrexate (MTX) is one of the most commonly used medications to treat rheumatoid arthritis (RA). However, the effect of MTX treatment on cellular immune responses remains incompletely understood. This raises concerns about the vulnerability of these patients to emerging infections and following vaccination.

Methods

In the current study, we investigated the impact of MTX treatment in patients with immune-mediated inflammatory disease on B and CD4 T cell SARS-CoV-2 vaccination responses. Eighteen patients with RA and two patients with psoriatic arthritis on MTX monotherapy were included, as well as 10 patients with RA without immunosuppressive treatment, and 29 healthy controls. CD4 T and B cell responses were analysed 7 days and 3–6 months after two SARS-CoV-2 messenger RNA vaccinations. High-dimensional flow cytometry analysis was used to analyse fresh whole blood, an activation-induced marker assay to measure antigen-specific CD4 T cells, and spike probes to study antigen-specific B cells.

Results

Seven days following two SARS-CoV-2 vaccinations, total B and T cell counts were similar between MTX-treated patients and controls. In addition, spike-specific B cell frequencies were unaffected. Remarkably, the frequency of antigen-specific CD4 T cells was reduced in patients using MTX and correlated strongly with anti-RBD IgG antibodies. These results suggest that decreased CD4 T cell activity may result in slower vaccination antibody responses in MTX-treated patients.

Conclusion

Taken together, MTX treatment reduces vaccine-induced CD4 T cell activation, which correlates with lower antibody responses.

Trial registration number

NL8900.
Eating traditional foods enhances diet quality among First Nations in Canada: an analysis using the Healthy Eating Food Index-2019 (HEFI-2019) and the Canadian Healthy Eating Index 2007 (C-HEI 2007)
Authors: Sebai Ines , Ing Amy , Nardocci Fusco Milena , Fediuk Karen , Sadik Tonio , Chan Hing Man , Batal Malek
Published: October 2024

Understanding the dietary patterns of First Nations is crucial for addressing health disparities and promoting well-being. Historical assaults (colonization and loss of control over their lands) have strongly altered dietary practices and impacted health outcomes for generations. Canada conducts regular surveys to assess the extent to which individuals adhere to dietary guidelines. However, Indigenous peoples living on reserves are excluded from these surveys. This study aims to assess the diet quality of First Nations adults using the Healthy Eating Food Index-2019 (HEFI-2019) and the Canadian Healthy Eating Index 2007 (C-HEI 2007) and identify their influencing factors. Data were collected from adults (19 years and older) across ninety-two First Nations communities throughout Canada. Participants provided information on sociodemographic factors and dietary intake using structured questionnaires and 24 h dietary recalls. Statistical analyses included mean scores and regression models to assess associations between dietary indices and influencing factors. The mean HEFI-2019 and C-HEI 2007 scores among First Nations adults were 35/80 and 49/100, respectively, indicating suboptimal adherence to dietary guidelines compared to the Canadian population. Factors such as region, age, sex, education level, number of working people in the household, smoking status, and traditional food intake significantly influenced diet quality. This study underscores the importance of understanding and improving the diet quality of First Nations adults as measured by HEFI-2019 and C-HEI 2007 scores. While acknowledging the low adherence to dietary guidelines, particularly in younger age groups, the study highlights the positive influence of traditional foods on diet quality within Indigenous communities.
Influence of joint stiffness on three-dimensional deformation mechanisms of pipelines under tunnel active face instability
Authors: Shi Jiangwei , Zhong Xiaochun , Lu Hu , Ni Xiaodong , Shi Chao
Published: October 2024

Although extensive research has been conducted in literature to investigate tunnelling effects on pipelines, the performance of existing jointed pipelines subjected to excessive tunnel movement has not been investigated. This study sets out to examine the effects of tunnel active instability on three-dimensional deformation patterns of existing pipelines using physical model tests. Tunnelling induced ground/pipeline settlement, joint rotation, and bending strain under various horizontal tunnel face movements are systematically explored. Results show that the tunnel face pressure at the active limit state is practically independent of tunnel cover to diameter ratio ( C/D). Existing pipelines that fall within a range of 0.1 D–0.5 D ahead of the tunnel face experience excessive settlements, significant bending strains and joint rotations. The measured peak settlement of pipelines at 0.3 D in front of the tunnel face exceeds the allowable limit, and the maximum settlements in the jointed pipeline are up to 2.67 times those observed in the continuous pipeline. Meanwhile, the joint rotation angle and settlement induced in the jointed pipeline are greatly affected by the C/D ratio and decrease by up to 55.5% and 22.0%, respectively, with an increasing C/D ratio from 1.0 to 1.5.
History of the Department of Chemistry at the University of Guelph
Authors: Thomas D.F. , Schwan A.L.
Published: October 2024

A review of the 150 year history of the Department of Chemistry at the University of Guelph is presented. The department was formed at the creation of the Ontario School of Agriculture in 1874. It was a central figure in the growth of the school into the Ontario Agricultural College. With the creation of the University of Guelph in 1964, the department developed into a modern research chemistry department, tasked with the instruction of chemistry for science students at the university. It continues to this day to be a key source of chemical research and education.
The influence of food web structure and foraging behaviour on visual system traits in a predatory freshwater fish
Authors: Guzzo Matthew M. , O'Connor Reilly , Novella-Talusan Lyle , Graham Brandon , Versteeg Evan , McMeans Bailey C. , Ridgway Mark , McCann Kevin S. , Laberge Frédéric
Published: October 2024

Lake trout ( Salvelinus namaycush Walbaum, 1792) forages across macrohabitats (offshore vs. nearshore) that likely pose different challenges to the visual system. We examined how differential foraging between macrohabitats challenges the visual system by sampling lake trout from four lakes with differing food web structures (e.g., presence/absence of cisco/smallmouth bass) and evaluating the association between nearshore energy use and visual traits. We found that lake trout foraging more nearshore had relatively larger eyes (corrected for body size) and larger optic tectum size only in lakes with the large pelagic cisco prey absent, while there was no association with these visual traits when cisco was present. Eye shape was not influenced by nearshore foraging. Our results suggest that nearshore foraging challenges the visual system more than offshore foraging but that this may be dependent on important food web structural attributes.
Constraints on metric-Palatini gravity from QPO data
Authors: Ghorani Elham , Mitra Samik , Rayimbaev Javlon , Puliçe Beyhan , Atamurotov Farruh , Abdujabbarov Ahmadjon , Demir Durmuş
Published: October 2024

In this work, we study metric-Palatini gravity extended by the antisymmetric part of the affine curvature. This gravity theory leads to general relativity plus a geometric Proca field. Using our previous construction of its static spherically-symmetric AdS solution (Eur Phys J. C 83(4):318, 2023), we perform a detailed analysis in this work using the observational quasiperiodic oscillations (QPOs) data. To this end, we use the latest data from stellar-mass black hole GRO J1655-40, intermediate-mass black hole in M82-X1, and the super-massive black hole in SgA* (our Milky Way) and perform a Monte-Carlo-Markov-Chain (MCMC) analysis to determine or bound the model parameters. Our results shed light on the allowed ranges of the Proca mass and other parameters. The results imply that our solutions can cover all three astrophysical black holes. Our analysis can also be extended to more general metric-affine gravity theories.
CYG$$\nu $$S: detecting solar neutrinos with directional gas time projection chambers
Authors: Lisotti Chiara , O’Hare Ciaran A. J. , Baracchini Elisabetta , Bashu Victoria U. , Bignell Lindsey J. , Dastgiri Ferdos , Ghrear Majd , Lane Gregory J. , McKie Lachlan J. , McNamara Peter C. , Torelli Samuele
Published: October 2024

Cygnus is a proposed global network of large-scale gas time projection chambers (TPCs) with the capability of directionally detecting nuclear and electron recoils at $$\gtrsim $$
≳
keV energies. The primary focus of Cygnus so far has been the detection of dark matter, with directional sensitivity providing a means of circumventing the so-called “neutrino fog”. However, the excellent background rejection and electron/nuclear recoil discrimination provided by the 3-dimensional reconstruction of ionisation tracks could turn the solar neutrino background into an interesting signal in its own right. For example, directionality would facilitate the simultaneous spectroscopy of multiple different flux sources. Here, we evaluate the possibility of measuring solar neutrinos using the same network of gas TPCs built from 10 m$$^3$$

3

-scale modules operating under conditions that enable simultaneous sensitivity to both dark matter and neutrinos. We focus in particular on electron recoils, which provide access to low-energy neutrino fluxes like pp, pep, $$^7$$

7

Be, and CNO. An appreciable event rate is already detectable in experiments consisting of a single 10 m$$^3$$

3

module, assuming standard fill gases such as CF$$_4$$

4

mixed with helium at atmospheric pressure. With total volumes around 1000 m$$^3$$

3

or higher, the TPC network could be complementary to dedicated neutrino observatories, whilst entering the dark-matter neutrino fog via the nuclear recoil channel. We evaluate the required directional performance and background conditions to observe, discriminate, and perform spectroscopy on neutrino events. We find that, under reasonable projections for planned technology that will enable 10–30-degree angular resolution and $$\sim 10$$

∼
10

% fractional energy resolution, Cygnus could be a competitive directional neutrino experiment.
Maximum entropy spectral analysis: an application to gravitational waves data analysis
Authors: Martini Alessandro , Schmidt Stefano , Ashton Gregory , Del Pozzo Walter
Published: October 2024

The maximum entropy spectral analysis (MESA) method, developed by Burg, offers a powerful tool for spectral estimation of a time-series. It relies on Jaynes’ maximum entropy principle, allowing the spectrum of a stochastic process to be inferred using the coefficients of an autoregressive process AR(p) of order p. A closed-form recursive solution provides estimates for both the autoregressive coefficients and the order p of the process. We provide a ready-to-use implementation of this algorithm in a Python package called , characterized through power spectral density (PSD) analysis on synthetic data with known PSD and comparisons of different criteria for stopping the recursion. Additionally, we compare the performance of our implementation with the ubiquitous Welch algorithm, using synthetic data generated from the GW150914 strain spectrum released by the LIGO-Virgo-Kagra collaboration. Our findings indicate that Burg’s method provides PSD estimates with systematically lower variance and bias. This is particularly manifest in the case of a small (O(5000)) number of data points, making Burg’s method most suitable to work in this regime. Since this is close to the typical length of analysed gravitational waves data, improving the estimate of the PSD in this regime leads to more reliable posterior profiles for the system under study. We conclude our investigation by utilising MESA, and its particularly easy parametrisation where the only free parameter is the order p of the AR process, to marginalise over the interferometers noise PSD in conjunction with inferring the parameters of GW150914
Thermoluminescence and kinetic parameters of irradiated dopant calcium aluminate nanophosphors
Authors: El-Faramawy Nabil , Salah Abeer , Farouk Shrouk
Published: October 2024

CaAl2O4:0.1% La nanoparticles doped with different concentrations of Mg were prepared using the sol–gel method and annealed at 400 °C, 500 °C, and 800 °C, respectively. The prepared nanophosphors show a highly crystalline orthorhombic structure with a crystallite size of about 81.2 nm. The optimum concentration of Mg dopants is about 0.7 mol%. The thermoluminescence (TL) glow curve of 11 Gy of beta-ray irradiated CaAl2O4:0.1% La:0.7% Mg measured at heating rates of 5 K/s has 16 overlapping glow peaks. The TL kinetic parameters for the prepared nanoparticles were evaluated using two different methods and they exhibited good agreement. Moreover, the linearity dose response of dosimetric peaks follows a good linearity up to 220 Gy in nanophosphor. The minimum detectable dose of the prepared nanophosphor was 125.7 mGy. The results of the prepared nanophosphors suggest good candidates for dosimetric applications.

Graphical abstract
Between the Booms: AI in Winter
Authors: Haigh Thomas
Published: October 2024

After people stopped caring, artificial intelligence got more interesting.
Object-Oriented Fixpoint Programming with Datalog
Authors: Klopp David , Erdweg Sebastian , Pacak André
Published: October 2024

Modern usages of Datalog exceed its original design purpose in scale and complexity.

In particular, Datalog lacks abstractions for code organization and reuse, making programs hard to maintain.

Is it possible to exploit abstractions and design patterns from object-oriented programming (OOP) while retaining a Datalog-like fixpoint semantics?

To answer this question, we design a new OOP language called OODL with common OOP features: dynamic object allocation, object identity, dynamic dispatch, and mutation.

However, OODL has a Datalog-like fixpoint semantics, such that recursive computations iterate until their result becomes stable.

We develop two semantics for OODL: a fixpoint interpreter and a compiler that translates OODL to Datalog.

Although the side effects found in OOP (object allocation and mutation) conflict with Datalog's fixpoint semantics, we can mostly resolve these incompatibilities through extensions of OODL.

Within fixpoint computations, we employ immutable algebraic data structures (e.g. case classes in Scala), rather than relying on object allocation, and we introduce monotonically mutable data types (mono types) to enable a relaxed form of mutation.

Our performance evaluation shows that the interpreter fails to solve fixpoint problems efficiently, whereas the compiled code exploits Datalog's semi-naïve evaluation.
Intensional Functions
Authors: Palmer Zachary , Filardo Nathaniel Wesley , Wu Ke
Published: October 2024

Functions in functional languages have a single elimination form — application — and cannot be compared, hashed, or subjected to other non-application operations. These operations can be approximated via defunctionalization: functions are replaced with first-order data and calls are replaced with invocations of a dispatch function. Operations such as comparison may then be implemented for these first-order data to approximate e.g. deduplication of continuations in algorithms such as unbounded searches. Unfortunately, this encoding is tedious, imposes a maintenance burden, and obfuscates the affected code. We introduce an alternative inintensional functions, a language feature which supports the definition of non-application operations in terms of a function’s definition site and closure-captured values. First-order data operations may be defined on intensional functions without burdensome code transformation. We give an operational semantics and type system and prove their formal properties. We further defineintensional monads, whose Kleisli arrows are intensional functions, enabling monadic values to be similarly subjected to additional operations.
Automating Unrealizability Logic: Hoare-Style Proof Synthesis for Infinite Sets of Programs
Authors: Nagy Shaan , Kim Jinwoo , Reps Thomas , D’Antoni Loris
Published: October 2024

Automated verification of all members of a (potentially infinite) set of programs has the potential to be useful in program synthesis, as well as in verification of dynamically loaded code, concurrent code, and language properties. Existing techniques for verification of sets of programs are limited in scope and unable to create or use interpretable or reusable information about sets of programs. The consequence is that one cannot learn anything from one verification problem that can be used in another. Unrealizability logic (UL), proposed by Kim et al. as the first Hoare-style proof system to prove properties over sets of programs (defined by a regular tree grammar), presents a theoretical framework that can express and use reusable insight. In particular, UL features
nonterminal summaries
---inductive facts that characterize recursive nonterminals (analogous to procedure summaries in Hoare logic). In this work, we design the first UL proof synthesis algorithm, implemented as Wuldo. Specifically, we decouple the problem of deciding how to apply UL rules from the problem of synthesizing/checking nonterminal summaries by computing proof structure in a fully syntax-directed fashion. We show that Wuldo, when provided nonterminal summaries, can express and prove verification problems beyond the reach of existing tools, including establishing how infinitely many programs behave on infinitely many inputs. In some cases, Wuldo can even synthesize the necessary nonterminal summaries. Moreover, Wuldo can reuse previously proven nonterminal summaries across verification queries, making verification 1.96 times as fast as when summaries are instead proven from scratch.
Compiler Support for Sparse Tensor Convolutions
Authors: Liu Peiming , Root Alexander J , Xu Anlun , Li Yinying , Kjolstad Fredrik , Bik Aart J.C.
Published: October 2024

This paper extends prior work on sparse tensor algebra compilers to generate asymptotically efficient code for tensor expressions with affine subscript expressions. Our technique enables compiler support for a wide range of sparse computations, including sparse convolutions and pooling that are widely used in ML and graphics applications. We propose an approach that gradually rewrites compound subscript expressions to simple subscript expressions with loops that exploit the sparsity pattern of the input sparse tensors. As a result, the time complexity of the generated kernels is bounded by the number of stored elements and not by the shape of the tensors. Our approach seamlessly integrates into existing frameworks and is compatible with recent advances in compilers for sparse computations, including the flexibility to efficiently handle arbitrary combinations of different sparse tensor formats. The implementation of our algorithm is open source and upstreamed to the MLIR sparse compiler. Experimental results show that our method achieves 19.5x speedup when compared with the state-of-the-art compiler-based method at 99.9% sparsity. The generated sparse kernels start to outperform dense convolution implementations at about 80% sparsity.
Iris-MSWasm: Elucidating and Mechanising the Security Invariants of Memory-Safe WebAssembly
Authors: Legoupil Maxime , Rousseau June , Georges Aïna Linn , Pichon-Pharabod Jean , Birkedal Lars
Published: October 2024

WebAssembly offers coarse-grained encapsulation guarantees via its module system, but does not support fine-grained sharing of its linear memory. MSWasm is a recent proposal which extends WebAssembly with fine-grained memory sharing via handles, a type of capability that guarantees spatial and temporal safety, and thus enables an expressive yet safe style of programming with flexible sharing.

In this paper, we formally validate the pen-and-paper design of MSWasm. To do so, we first define MSWasmCert, a mechanisation of MSWasm that makes it a fully-defined, conservative extension of WebAssembly 1.0, including the module system. We then develop Iris-MSWasm, a foundational reasoning framework for MSWasm composed of a separation logic to reason about known code, and a logical relation to reason about unknown, potentially adversarial code. Iris-MSWasm thereby makes explicit a key aspect of the implicit universal contract of MSWasm: robust capability safety. We apply Iris-MSWasm to reason about key use cases of handles, in which the effect of calling an unknown function is bounded by robust capability safety. Iris-MSWasm thus works as a framework to prove complex security properties of MSWasm programs, and provides a foundation to evaluate the language-level guarantees of MSWasm.
Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs
Authors: Cassano Federico , Gouwar John , Lucchetti Francesca , Schlesinger Claire , Freeman Anders , Anderson Carolyn Jane , Feldman Molly Q , Greenberg Michael , Jangda Abhinav , Guha Arjun
Published: October 2024

Over the past few years, Large Language Models of Code (Code LLMs) have started to have a significant impact on programming practice. Code LLMs are also emerging as building blocks for research in programming languages and software engineering. However, the quality of code produced by a Code LLM varies significantly by programming language. Code LLMs produce impressive results on high-resource programming languages that are well represented in their training data (e.g., Java, Python, or JavaScript), but struggle with low-resource languages that have limited training data available (e.g., OCaml, Racket, and several others).

This paper presents an effective approach for boosting the performance of Code LLMs on low-resource languages using semi-synthetic data. Our approach, called MultiPL-T, generates high-quality datasets for low-resource languages, which can then be used to fine-tune any pretrained Code LLM. MultiPL-T translates training data from high-resource languages into training data for low-resource languages in the following way. 1) We use a Code LLM to synthesize unit tests for commented code from a high-resource source language, filtering out faulty tests and code with low test coverage. 2) We use a Code LLM to translate the code from the high-resource source language to a target low-resource language. This gives us a corpus of candidate training data in the target language, but many of these translations are wrong. 3) We use a lightweight compiler to compile the test cases generated in (1) from the source language to the target language, which allows us to filter our obviously wrong translations. The result is a training corpus in the target low-resource language where all items have been validated with test cases. We apply this approach to generate tens of thousands of new, validated training items for five low-resource languages: Julia, Lua, OCaml, R, and Racket, using Python as the source high-resource language. Furthermore, we use an open Code LLM (StarCoderBase) with open training data (The Stack), which allows us to decontaminate benchmarks, train models without violating licenses, and run experiments that could not otherwise be done.

Using datasets generated with MultiPL-T, we present fine-tuned versions of StarCoderBase and Code Llama for Julia, Lua, OCaml, R, and Racket that outperform other fine-tunes of these base models on the natural language to code task. We also present Racket fine-tunes for two very recent models, DeepSeek Coder and StarCoder2, to show that MultiPL-T continues to outperform other fine-tuning approaches for low-resource languages. The MultiPL-T approach is easy to apply to new languages, and is significantly more efficient and effective than alternatives such as training longer.
WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models
Authors: Yang Chenyuan , Deng Yinlin , Lu Runyu , Yao Jiayi , Liu Jiawei , Jabbarvand Reyhaneh , Zhang Lingming
Published: October 2024

Compiler correctness is crucial, as miscompilation can falsify program behaviors, leading to serious consequences over the software supply chain. In the literature, fuzzing has been extensively studied to uncover compiler defects. However, compiler fuzzing remains challenging: Existing arts focus on black- and grey-box fuzzing, which generates test programs without sufficient understanding of internal compiler behaviors. As such, they often fail to construct test programs to exercise intricate optimizations. Meanwhile, traditional white-box techniques, such as symbolic execution, are computationally inapplicable to the giant codebase of compiler systems. Recent advances demonstrate that Large Language Models (LLMs) excel in code generation/understanding tasks and even have achieved state-of-the-art performance in black-box fuzzing. Nonetheless, guiding LLMs with compiler source-code information remains a missing piece of research in compiler testing. To this end, we propose WhiteFox, the first white-box compiler fuzzer using LLMs with source-code information to test compiler optimization, with a spotlight on detecting deep logic bugs in the emerging deep learning (DL) compilers. WhiteFox adopts a multi-agent framework: (i) an LLM-based analysis agent examines the low-level optimization source code and produces requirements on the high-level test programs that can trigger the optimization; (ii) an LLM-based generation agent produces test programs based on the summarized requirements. Additionally, optimization-triggering tests are also used as feedback to further enhance the test generation prompt on the fly. Our evaluation on the three most popular DL compilers (i.e., PyTorch Inductor, TensorFlow-XLA, and TensorFlow Lite) shows that WhiteFox can generate high-quality test programs to exercise deep optimizations requiring intricate conditions, practicing up to 8 times more optimizations than state-of-the-art fuzzers. To date, WhiteFox has found in total 101 bugs for the compilers under test, with 92 confirmed as previously unknown and 70 already fixed. Notably, WhiteFox has been recently acknowledged by the PyTorch team, and is in the process of being incorporated into its development workflow. Finally, beyond DL compilers, WhiteFox can also be adapted for compilers in different domains, such as LLVM, where WhiteFox has already found multiple bugs.
HiPy: Extracting High-Level Semantics from Python Code for Data Processing
Authors: Jungmair Michael , Engelke Alexis , Giceva Jana
Published: October 2024

Data science workloads frequently include Python code, but Python's dynamic nature makes efficient execution hard. Traditional approaches either treat Python as a black box, missing out on optimization potential, or are limited to a narrow domain. However, a deep and efficient integration of user-defined Python code into data processing systems requires extracting the semantics of the entire Python code.

In this paper, we propose a novel approach for extracting the high-level semantics by transforming general Python functions into program generators that generate a statically-typed IR when executed.

The extracted IR then allows for high-level, domain-specific optimizations and the generation of efficient C++ code. With our prototype implementation, HiPy, we achieve single-threaded speedups of 2-20x for many workloads. Furthermore, HiPy is also capable of accelerating Python code in other domains like numerical data, where it can sometimes even outperform specialized compilers.
Quantitative Weakest Hyper Pre: Unifying Correctness and Incorrectness Hyperproperties via Predicate Transformers
Authors: Zhang Linpeng , Zilberstein Noam , Kaminski Benjamin Lucien , Silva Alexandra
Published: October 2024

We present a novel weakest pre calculus for reasoning about quantitative hyperproperties over nondeterministic and probabilistic programs. Whereas existing calculi allow reasoning about the expected value that a quantity assumes after program termination from a single initial state, we do so for initial sets of states or initial probability distributions. We thus (i) obtain a weakest pre calculus for hyper Hoare logic and (ii) enable reasoning about so-called hyperquantities which include expected values but also quantities (e.g. variance) out of scope of previous work. As a byproduct, we obtain a novel strongest post for weighted programs that extends both existing strongest and strongest liberal post calculi. Our framework reveals novel dualities between forward and backward transformers, correctness and incorrectness, as well as nontermination and unreachability.
Plume: Efficient and Complete Black-Box Checking of Weak Isolation Levels
Authors: Liu Si , Gu Long , Wei Hengfeng , Basin David
Published: October 2024

Modern databases embrace weak isolation levels to cater for highly available transactions. However, weak isolation bugs have recently manifested in many production databases. This raises the concern of whether database implementations actually deliver their promised isolation guarantees in practice. In this paper we present Plume, the first efficient, complete, black-box checker for weak isolation levels. Plume builds on modular, fine-grained, transactional anomalous patterns, with which we establish sound and complete characterizations of representative weak isolation levels, including read committed, read atomicity, and transactional causal consistency. Plume leverages a novel combination of two techniques, vectors and tree clocks, to accelerate isolation checking. Our extensive assessment shows that Plume can reproduce all known violations in a large collection of anomalous database execution histories, detect new isolation bugs in three production databases along with informative counterexamples, find more weak isolation anomalies than the state-of-the-art checkers, and efficiently validate isolation guarantees under a wide variety of workloads.
Automating Pruning in Top-Down Enumeration for Program Synthesis Problems with Monotonic Semantics
Authors: Johnson Keith J.C. , Krishnan Rahul , Reps Thomas , D’Antoni Loris
Published: October 2024

In top-down enumeration for program synthesis, abstraction-based pruning uses an abstract domain to approximate the set of possible values that a partial program, when completed, can output on a given input. If the set does not contain the desired output, the partial program and all its possible completions can be pruned. In its general form, abstraction-based pruning requires manually designed, domain-specific abstract domains and semantics, and thus has only been used in domain-specific synthesizers.

This paper provides sufficient conditions under which a form of abstraction-based pruning can be automated for arbitrary synthesis problems in the general-purpose Semantics-Guided Synthesis (SemGuS) framework without requiring manually-defined abstract domains. We show that if the semantics of the language for which we are synthesizing programs exhibits some monotonicity properties, one can obtain an abstract interval-based semantics for free from the concrete semantics of the programming language, and use such semantics to effectively prune the search space. We also identify a condition that ensures such abstract semantics can be used to compute a precise abstraction of the set of values that a program derivable from a given hole in a partial program can produce. These precise abstractions make abstraction-based pruning more effective.

We implement our approach in a tool, Moito, which can tackle synthesis problems defined in the SemGuS framework. Moito can automate interval-based pruning without any a-priori knowledge of the problem domain, and solve synthesis problems that previously required domain-specific, abstraction-based synthesizers— e.g., synthesis of regular expressions, CSV file schema, and imperative programs from examples.
On the Expressive Power of Languages for Static Variability
Authors: Bittner Paul Maximilian , Schultheiß Alexander , Moosherr Benjamin , Young Jeffrey M. , Teixeira Leopoldo , Walkingshaw Eric , Ataei Parisa , Thüm Thomas
Published: October 2024

Variability permeates software development to satisfy ever-changing requirements and mass-customization needs. A prime example is the Linux kernel, which employs the C preprocessor to specify a set of related but distinct kernel variants. To study, analyze, and verify variational software, several formal languages have been proposed. For example, the choice calculus has been successfully applied for type checking and symbolic execution of configurable software, while other formalisms have been used for variational model checking, change impact analysis, among other use cases. Yet, these languages have not been formally compared, hence, little is known about their relationships. Crucially, it is unclear to what extent one language subsumes another, how research results from one language can be applied to other languages, and which language is suitable for which purpose or domain. In this paper, we propose a formal framework to compare the expressive power of languages for static (i.e., compile-time) variability. By establishing a common semantic domain to capture a widely used intuition of explicit variability, we can formulate the basic, yet to date neglected, properties of soundness, completeness, and expressiveness for variability languages. We then prove the (un)soundness and (in)completeness of a range of existing languages, and relate their ability to express the same variational systems. We implement our framework as an extensible open source Agda library in which proofs act as correct compilers between languages or differencing algorithms. We find different levels of expressiveness as well as complete and incomplete languages w.r.t. our unified semantic domain, with the choice calculus being among the most expressive languages.
Programmable MCMC with Soundly Composed Guide Programs
Authors: Pham Long , Wang Di , Saad Feras A. , Hoffmann Jan
Published: October 2024

Probabilistic programming languages (PPLs) provide language support for expressing flexible probabilistic models and solving Bayesian inference problems. PPLs with
programmable inference
make it possible for users to obtain improved results by customizing inference engines using
guide
programs that are tailored to a corresponding
model
program. However, errors in guide programs can compromise the statistical soundness of the inference. This article introduces a novel coroutine-based framework for verifying the correctness of user-written guide programs for a broad class of Markov chain Monte Carlo (MCMC) inference algorithms. Our approach rests on a novel type system for describing communication protocols between a model program and a sequence of guides that each update only a subset of random variables. We prove that, by translating guide types to context-free processes with finite norms, it is possible to check structural type equality between models and guides in polynomial time. This connection gives rise to an efficient
type-inference algorithm
for probabilistic programs with flexible constructs such as general recursion and branching. We also contribute a
coverage-checking algorithm
that verifies the support of sequentially composed guide programs agrees with that of the model program, which is a key soundness condition for MCMC inference with multiple guides. Evaluations on diverse benchmarks show that our type-inference and coverage-checking algorithms efficiently infer types and detect sound and unsound guides for programs that existing static analyses cannot handle.
MEA2: A Lightweight Field-Sensitive Escape Analysis with Points-to Calculation for Golang
Authors: Ding Boyao , Li Qingwei , Zhang Yu , Tang Fugen , Chen Jinbao
Published: October 2024

Escape analysis plays a crucial role in garbage-collected languages as it enables the allocation of non-escaping variables on the stack by identifying the dynamic lifetimes of objects and pointers. This helps in reducing heap allocations and alleviating garbage collection pressure. However, Go, as a garbage-collected language, employs a fast yet conservative escape analysis, which is field-insensitive and omits point-to-set calculation to expedite compilation. This results in more variables being allocated on the heap. Empirical statistics reveal that field access and indirect memory access are prevalent in real-world Go programs, suggesting potential opportunities for escape analysis to enhance program performance. In this paper, we propose MEA
2
, an escape analysis framework atop GoLLVM (an LLVM-based Go compiler), which combines field sensitivity and points-to analysis. Moreover, a novel generic function summary representation is designed to facilitate fast inter-procedural analysis. We evaluated it by using MEA
2
to perform stack allocation in 12 wildly-use open-source projects. The results show that, compared to Go’s escape analysis, MEA
2
can reduce heap allocation sites by 7.9
Law and Order for Typestate with Borrowing
Authors: Saffrich Hannes , Nishida Yuki , Thiemann Peter
Published: October 2024

Typestate systems are notoriously complex as they require

sophisticated machinery for tracking aliasing.

We propose a new, transition-oriented foundation for typestate in the

setting of impure functional programming.

Our approach relies on ordered types for simple alias tracking and

its formalization draws on work on bunched implications.

Yet, we support a flexible notion of borrowing in the presence of typestate.

Our core calculus comes with a notion of resource types indexed by an

ordered partial monoid that models abstract state transitions.

We prove syntactic type soundness with respect to a

resource-instrumented semantics.

We give an algorithmic version of our type system and prove its

soundness. Algorithmic typing facilitates a

simple surface language that does not expose tedious details of

ordered types. We implemented a typechecker for the surface language

along with an interpreter for the core language.
A Runtime System for Interruptible Query Processing: When Incremental Computing Meets Fine-Grained Parallelism
Authors: Eymer Jeff , Dexter Philip , Raskind Joseph , Liu Yu David
Published: October 2024

Online data services have stringent performance requirement and must tolerate workload fluctuation. This paper introduces PitStop, a new query language runtime design built on the idea of interruptible query processing: the time-consuming task of data inspection for processing each query or update may be interrupted and resumed later at the boundary of fine-grained data partitions. This counter-intuitive idea enables a novel form of fine-grained concurrency while preserving sequential consistency. We build PitStop through modifying the language runtime of Cypher, the query language of a state-of-the-art graph database, Neo4j. Our evaluation on the Google Cloud shows that PitStop can outperform unmodified Neo4j during workload fluctuation, with reduced latency and increased throughput.
Concurrent Data Structures Made Easy
Authors: Le Callista , Gopinathan Kiran , Lee Koon Wen , Gilbert Seth , Sergey Ilya
Published: October 2024

Design of an efficient thread-safe concurrent data structure is a balancing act between its implementation complexity and performance. Lock-based concurrent data structures, which are relatively easy to derive from their sequential counterparts and to prove thread-safe, suffer from poor throughput under even light multi-threaded workload. At the same time, lock-free concurrent structures allow for high throughput, but are notoriously difficult to get right and require careful reasoning to formally establish their correctness.

In this work, we explore a solution to this conundrum based on a relatively old idea of batch parallelism---an approach for designing high-throughput concurrent data structures via a simple insight: efficiently processing a batch of a priori known operations in parallel is easier than optimising performance for a stream of arbitrary asynchronous requests. Alas, batch-parallel structures have not seen wide practical adoption due to (i) the inconvenience of having to structure multi-threaded programs to explicitly group operations and (ii) the lack of a systematic methodology to implement batch-parallel structures as simply as lock-based ones.

We present OBatcher---a Multicore OCaml library that streamlines the design, implementation, and usage of batch-parallel structures. OBatcher solves the first challenge (how to use) by suggesting a new lightweight implicit batching design pattern that is built on top of generic asynchronous programming mechanisms. The second challenge (how to implement) is addressed by identifying a family of strategies for converting common sequential structures into the corresponding efficient batch-parallel versions, and by providing a library of functors that embody those strategies. We showcase OBatcher with a diverse set of benchmarks ranging from Red-Black and AVL trees to van Emde Boas trees, skip lists, and a thread-safe implementation of a Datalog solver. Our evaluation of all the implementations on large asynchronous workloads shows that (a) they consistently outperform the corresponding coarse-grained lock-based implementations---the only ones available in OCaml to date, and that (b) their throughput scales reasonably with the number of processors.
Rustlantis: Randomized Differential Testing of the Rust Compiler
Authors: Wang Qian , Jung Ralf
Published: October 2024

Compilers are at the core of all computer architecture. Their middle-end and back-end are full of subtle code that is easy to get wrong. At the same time, the consequences of compiler bugs can be severe. Therefore, it is important that we develop techniques to increase our confidence in compiler correctness, and to help find the bugs that inevitably happen. One promising such technique that has successfully found many compiler bugs in the past is
randomized differential testing
, a fuzzing approach whereby the same program is executed with different compilers or different compiler settings to detect any unexpected differences in behavior. We present
Rustlantis
, the first fuzzer for the Rust programming language that is able to find new correctness bugs in the official Rust compiler. To avoid having to deal with Rust’s strict type and borrow checker, Rustlantis directly generates MIR, the central IR of the Rust compiler for optimizations. The program generation strategy of Rustlantis is a combination of statically tracking the state of the program, obscuring the program state for the compiler, and
decoy blocks
to lead optimizations astray. This has allowed us to identify 22 previously unknown bugs in the Rust compiler, most of which have been fixed.