Code Translation with Compiler Representations

In this paper, we leverage low-level compiler intermediate representations (IR) to improve code translation. Traditional transpilers rely on syntactic information and handcrafted rules, which limits their applicability and produces unnatural-looking code. Applying neural machine translation (NMT) approaches to code has successfully broadened the set of programs on which one can get a natural-looking translation. However, they treat the code as sequences of text tokens, and still do not differentiate well enough between similar pieces of code which have different semantics in different languages. The consequence is low quality translation, reducing the practicality of NMT, and stressing the need for an approach significantly increasing its accuracy. Here we propose to augment code translation with IRs, specifically LLVM IR, with results on the C++, Java, Rust, and Go languages. Our method improves upon the state of the art for unsupervised code translation, increasing the number of correct translations by 11% on average, and up to 79% for the Java -> Rust pair with greedy decoding. With beam search, it increases the number of correct translations by 5.5% in average. We extend previous test sets for code translation, by adding hundreds of Go and Rust functions. Additionally, we train models with high performance on the problem of IR decompilation, generating programming source code from IR, and study using IRs as intermediary pivot for translation.Comment: 9 page

SALSA VERDE: a machine learning attack on Learning With Errors with sparse small secrets

Learning with Errors (LWE) is a hard math problem used in post-quantum cryptography. Homomorphic Encryption (HE) schemes rely on the hardness of the LWE problem for their security, and two LWE-based cryptosystems were recently standardized by NIST for digital signatures and key exchange (KEM). Thus, it is critical to continue assessing the security of LWE and specific parameter choices. For example, HE uses secrets with small entries, and the HE community has considered standardizing small sparse secrets to improve efficiency and functionality. However, prior work, SALSA and PICANTE, showed that ML attacks can recover sparse binary secrets. Building on these, we propose VERDE, an improved ML attack that can recover sparse binary, ternary, and narrow Gaussian secrets. Using improved preprocessing and secret recovery techniques, VERDE can attack LWE with larger dimensions ($n=512$) and smaller moduli ($\log_2 q=12$ for $n=256$), using less time and power. We propose novel architectures for scaling. Finally, we develop a theory that explains the success of ML LWE attacks.Comment: 18 pages, accepted to NeurIPS 202

SALSA: Attacking Lattice Cryptography with Transformers

Currently deployed public-key cryptosystems will be vulnerable to attacks by full- scale quantum computers. Consequently, quantum resistant cryptosystems are in high demand, and lattice-based cryptosystems, based on a hard problem known as Learning With Errors (LWE), have emerged as strong contenders for standardization. In this work, we train transformers to perform modular arithmetic and combine half-trained models with statistical cryptanalysis techniques to propose SALSA: a machine learning attack on LWE-based cryptographic schemes. SALSA can fully recover secrets for small-to-mid size LWE instances with sparse binary secrets, and may scale to attack real-world LWE-based cryptosystems

SALSA PICANTE: a machine learning attack on LWE with binary secrets

Learning with Errors (LWE) is a hard math problem underpinning many proposed post-quantum cryptographic (PQC) systems. The only PQC Key Exchange Mechanism (KEM) standardized by NIST is based on module~LWE, and current publicly available PQ Homomorphic Encryption (HE) libraries are based on ring LWE. The security of LWE-based PQ cryptosystems is critical, but certain implementation choices could weaken them. One such choice is sparse binary secrets, desirable for PQ HE schemes for efficiency reasons. Prior work, SALSA, demonstrated a machine learning-based attack on LWE with sparse binary secrets in small dimensions ($n \le 128$) and low Hamming weights ($h \le 4$). However, this attack assumes access to millions of eavesdropped LWE samples and fails at higher Hamming weights or dimensions. We present PICANTE, an enhanced machine learning attack on LWE with sparse binary secrets, which recovers secrets in much larger dimensions (up to $n=350$) and with larger Hamming weights (roughly $n/10$, and up to $h=60$ for $n=350$). We achieve this dramatic improvement via a novel preprocessing step, which allows us to generate training data from a linear number of eavesdropped LWE samples ($4n$) and changes the distribution of the data to improve transformer training. We also improve the secret recovery methods of SALSA and introduce a novel cross-attention recovery mechanism allowing us to read off the secret directly from the trained models. While PICANTE does not threaten NIST\u27s proposed LWE standards, it demonstrates significant improvement over SALSA and could scale further, highlighting the need for future investigation into machine learning attacks on LWE with sparse binary secrets

Molecular investigation of Tuscan sweet cherries sampled over three years: gene expression analysis coupled to metabolomics and proteomics.

peer reviewedSweet cherry (Prunus avium L.) is a stone fruit widely consumed and appreciated for its organoleptic properties, as well as its nutraceutical potential. We here investigated the characteristics of six non-commercial Tuscan varieties of sweet cherry maintained at the Regional Germplasm Bank of the CNR-IBE in Follonica (Italy) and sampled ca. 60 days post-anthesis over three consecutive years (2016-2017-2018). We adopted an approach merging genotyping and targeted gene expression profiling with metabolomics. To complement the data, a study of the soluble proteomes was also performed on two varieties showing the highest content of flavonoids. Metabolomics identified the presence of flavanols and proanthocyanidins in highest abundance in the varieties Morellona and Crognola, while gene expression revealed that some differences were present in genes involved in the phenylpropanoid pathway during the 3 years and among the varieties. Finally, proteomics on Morellona and Crognola showed variations in proteins involved in stress response, primary metabolism and cell wall expansion. To the best of our knowledge, this is the first multi-pronged study focused on Tuscan sweet cherry varieties providing insights into the differential abundance of genes, proteins and metabolites

A community effort in SARS-CoV-2 drug discovery.

peer reviewedThe COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against Covid-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.R-AGR-3826 - COVID19-14715687-CovScreen (01/06/2020 - 31/01/2021) - GLAAB Enric

SALSA PICANTE: a machine learning attack on LWE with binary secrets

Learning with Errors (LWE) is a hard math problem underpinning many proposed post-quantum cryptographic (PQC) systems. The only PQC Key Exchange Mechanism (KEM) standardized by NIST is based on module~LWE, and current publicly available PQ Homomorphic Encryption (HE) libraries are based on ring LWE. The security of LWE-based PQ cryptosystems is critical, but certain implementation choices could weaken them. One such choice is sparse binary secrets, desirable for PQ HE schemes for efficiency reasons. Prior work, SALSA, demonstrated a machine learning-based attack on LWE with sparse binary secrets in small dimensions ($n \le 128$) and low Hamming weights ($h \le 4$). However, this attack assumes access to millions of eavesdropped LWE samples and fails at higher Hamming weights or dimensions. We present PICANTE, an enhanced machine learning attack on LWE with sparse binary secrets, which recovers secrets in much larger dimensions (up to $n=350$) and with larger Hamming weights (roughly $n/10$, and up to $h=60$ for $n=350$). We achieve this dramatic improvement via a novel preprocessing step, which allows us to generate training data from a linear number of eavesdropped LWE samples ($4n$) and changes the distribution of the data to improve transformer training. We also improve the secret recovery methods of SALSA and introduce a novel cross-attention recovery mechanism allowing us to read off the secret directly from the trained models. While PICANTE does not threaten NIST's proposed LWE standards, it demonstrates significant improvement over SALSA and could scale further, highlighting the need for future investigation into machine learning attacks on LWE with sparse binary secrets.Comment: 15 pages, 6 figures, 17 table

Exercise-Induced Cardiac Fatigue in Soldiers Assessed by Echocardiography

International audienceBackground: Echocardiographic signs of exercise-induced cardiac fatigue (EICF) have been described after strenuous endurance exercise. Nevertheless, few data are available on the effects of repeated strenuous exercise, especially when associated with other constraints as sleep deprivation or mental stress which occur during military selection boot camps. Furthermore, we aimed to study the influence of experience and training level on potential EICF signs.Methods: Two groups of trained soldiers were included, elite soldiers from the French Navy Special Forces (elite; n = 20) and non-elite officer cadets from a French military academy (non-elite; n = 38). All underwent echocardiography before and immediately after exposure to several days of uninterrupted intense exercise during their selection boot camps. Changes in myocardial morphology and function of the 4 cardiac chambers were assessed.Results: Exercise-induced decrease in right and left atrial and ventricular functions were demonstrated with 2D-strain parameters in both groups. Indeed, both atrial reservoir strain, RV and LV longitudinal strain and LV global constructive work were altered. Increase in LV mechanical dispersion assessed by 2D-strain and alteration of conventional parameters of diastolic function (increase in E/e' and decrease in e') were solely observed in the non-elite group. Conventional parameters of LV and RV systolic function (LVEF, RVFAC, TAPSE, s mitral, and tricuspid waves) were not modified.Conclusions: Alterations of myocardial functions are observed in soldiers after uninterrupted prolonged intense exercise performed during selection boot camps. These alterations occur both in elite and non-elite soldiers. 2D-strain is more sensitive to detect EICF than conventional echocardiographic parameters

Identification of Novel Candidate Genes Involved in Apple Cuticle Integrity and Russeting-Associated Triterpene Synthesis Using Metabolomic, Proteomic, and Transcriptomic Data

Apple russeting develops on the fruit surface when skin integrity has been lost. It induces a modification of fruit wax composition, including its triterpene profile. In the present work, we studied two closely related apple varieties, ‘Reinette grise du Canada’ and ‘Reinette blanche du Canada’, which display russeted and non-russeted skin phenotypes, respectively, during fruit development. To better understand the molecular events associated with russeting and the differential triterpene composition, metabolomics data were generated using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) and combined with proteomic and transcriptomic data. Our results indicated lower expression of genes linked to cuticle biosynthesis (cutin and wax) in russet apple throughout fruit development, along with an alteration of the specialized metabolism pathways, including triterpene and phenylpropanoid. We identified a lipid transfer protein (LTP3) as a novel player in cuticle formation, possibly involved in the transport of both cutin and wax components in apple skin. Metabolomic data highlighted for the first time a large diversity of triterpene-hydroxycinnamates in russeted tissues, accumulation of which was highly correlated with suberin-related genes, including some enzymes belonging to the BAHD (HXXXD-motif) acyltransferase family. Overall, this study increases our understanding about the crosstalk between triterpene and suberin pathways

Exploring Ru compatibility with Al-Ge eutectic wafer bonding

We explore compatibility of Ru with Al-Ge eutectic wafer bonding. We first present experiments to check for the presence of Ru ternary alloy poisoning inhibiting Al-Ge melting as well as evaluations of Al-Ge melt wettability on Ru and diffusion outcomes following bond-simulating anneals. Results show that Ru is stable with no observed microstructural changes or dissolution in the melt, indicating no ternary poisoning for the applied thermal budget. Ru was found to act as an effective barrier offering good melt wettability in all considered configurations with Al and Ge. From inspection of the binary constituents of Al-Ge-Ru we propose that Al-Ge eutectic melting temperature will decrease marginally for Ru contamination in a 1-2% range before a drastic increase in melting temperature (>10°C/% Ru) at higher Ru compositions. We then demonstrate wafer-level packaged 200 mm devices and MEMS with strong bond outcomes of devices bearing Ru contacts. We conclude that Ru has high compatibility with Al-Ge eutectic bonding