Consensual Resilient Control: Stateless Recovery of Stateful Controllers

Safety-critical systems have to absorb accidental and malicious faults to obtain high mean-times-to-failures (MTTFs). Traditionally, this is achieved through re-execution or replication. However, both techniques come with significant overheads, in particular when cold-start effects are considered. Such effects occur after replicas resume from checkpoints or from their initial state. This work aims at improving on the performance of control-task replication by leveraging an inherent stability of many plants to tolerate occasional control-task deadline misses and suggests masking faults just with a detection quorum. To make this possible, we have to eliminate cold-start effects to allow replicas to rejuvenate during each control cycle. We do so, by systematically turning stateful controllers into instants that can be recovered in a stateless manner. We highlight the mechanisms behind this transformation, how it achieves consensual resilient control, and demonstrate on the example of an inverted pendulum how accidental and maliciously-induced faults can be absorbed, even if control tasks run in less predictable environments

Tropical Backpropagation

This work introduces tropicalization, a novel technique that delivers tropical neural networks as tropical limits of deep ReLU networks. Tropicalization transfers the initial weights from real numbers to those in the tropical semiring while maintain- ing the underlying graph of the network. After verifying that tropicalization will not affect the classification capacity of deep neural networks, this study introduces a tropical reformulation of backpropagation via tropical linear algebra. Tropical arithmetic replaces multiplication operations in the network with additions and addition operations with max, and therefore, theoretically, reduces the algorithmic complexity during the training and inference phase. We demonstrate the latter by simulating the tensor multiplication underlying the feed-forward process of state- of-the-art trained neural network architectures and compare the standard forward pass of the models with the tropical ones. Our benchmark results show that tropi- calization speeds up inference by 50 %. Hence, we conclude that tropicalization bears the potential to reduce the training times of large neural networks drastically

Reading Aloud and First Language Development: A Systematic Review

Reading aloud appears to be an important lever for improving language acquisition and development in early childhood, and later in life it strengthens many sub-dimensions of language. However, the availability of numerous variations on reading training, shaped by different methodologies and different lengths of exposure make it difficult to determine the best approaches to follow. The aim of this review is to identify the available literature contributions that examine the association between mediated reading training, first language development and the acquisition of new vocabulary, including other components that could be improved by these interventions, such as cognitive function, emergent literacy and adult-child verbal interactions. The purpose is to compare research highlighting their fundamental characteristics, tools, duration and methodologies used in order to point out the effects that the practice of reading aloud produces on the acquisition and the enhancement of language, particularly in the age of language development. The analysis of the 51 articles included aims to identify the most effective reading strategies in terms of practices, timing and methods, able to produce the most significant gains in the language area

Clinical predictive factors of pathologic complete response in locally advanced rectal cancer

Background: Predictive factors of pathologic complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) are still not identified. The purpose of this study was to define them. Materials and Methods: Data from consecutive LARC patients treated between January 2008 and June 2014 at our Institution were included in the analysis. All patients were treated with a long course of nCRT. Demographics, initial diagnosis and tumor extension details, as well as treatment modalities characteristics were included in the univariate and logistic regression analysis. Results: In total 99 patients received nCRT, of whom 23 patients (23.2%) achieved pCR. Patients with and without pCR were similar in term of age, sex, comobidities, BMI and tumor characteristics. Multivariate logistic regression indicated that pre-treatment tumor size <= 5 cm was a significant predictor for pCR (p = 0.035), whereas clinical N stage only showed a positive trend (p = 0.084). Conclusions: Tumor size at diagnosis could be used to predict pCR, and thus to individualize therapy in LARC patients management. Validation in other studies is needed

Consensual Resilient Control: Stateless Recovery of Stateful Controllers

peer reviewedSafety-critical systems have to absorb accidental and malicious faults to obtain high mean-times-to-failures (MTTFs). Traditionally, this is achieved through re-execution or replication. However, both techniques come with significant overheads, in particular when cold-start effects are considered. Such effects occur after replicas resume from checkpoints or from their initial state. This work aims at improving on the performance of control-task replication by leveraging an inherent stability of many plants to tolerate occasional control-task deadline misses and suggests masking faults just with a detection quorum. To make this possible, we have to eliminate cold-start effects to allow replicas to rejuvenate during each control cycle. We do so, by systematically turning stateful controllers into instants that can be recovered in a stateless manner. We highlight the mechanisms behind this transformation, how it achieves consensual resilient control, and demonstrate on the example of an inverted pendulum how accidental and maliciously-induced faults can be absorbed, even if control tasks run in less predictable environments

Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs

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 a building block for research in programming languages and software engineering. However, the quality of code produced by a Code LLM varies significantly by programming languages. Code LLMs produce impressive results on programming languages that are well represented in their training data (e.g., Java, Python, or JavaScript), but struggle with low-resource languages, like OCaml and Racket. This paper presents an effective approach for boosting the performance of Code LLMs on low-resource languages using semi-synthetic data. Our approach generates high-quality datasets for low-resource languages, which can then be used to fine-tune any pretrained Code LLM. Our approach, called MultiPL-T, translates training data from high-resource languages into training data for low-resource languages. We apply our approach to generate tens of thousands of new, validated training items for Racket, OCaml, and Lua from Python. Moreover, we use an open dataset (The Stack) and model (StarCoderBase), which allow us to decontaminate benchmarks and train models on this data without violating the model license. With MultiPL-T generated data, we present fine-tuned versions of StarCoderBase that achieve state-of-the-art performance for Racket, OCaml, and Lua on benchmark problems. For Lua, our fine-tuned model achieves the same performance as StarCoderBase as Python -- a very high-resource language -- on the MultiPL-E benchmarks. For Racket and OCaml, we double their performance on MultiPL-E, bringing their performance close to higher-resource languages such as Ruby and C#

The 75-Gram Glucose Load in Pregnancy

OBJECTIVE—To investigate, in pregnant women without gestational diabetes mellitus (GDM), the relation among obstetric/demographic characteristics; fasting, 1-h, and 2-h plasma glucose values resulting from a 75-g glucose load; and the risk of abnormal neonatal anthropometric features and then to verify the presence of a threshold glucose value for a 75-g glucose load above which there is an increased risk for abnormal neonatal anthropometric characteristics. RESEARCH DESIGN AND METHODS—The study group consisted of 829 Caucasian pregnant women with singleton pregnancy who had no history of pregestational diabetes or GDM, who were tested for GDM with a 75-g, 2-h glucose load, used as a glucose challenge test, in two periods of pregnancy (early, 16–20 weeks; late, 26–30 weeks), and who did not meet the criteria for a GDM diagnosis. In the newborns, the following abnormal anthropometric characteristics were considered as outcome measures: cranial/thoracic circumference (CC/TC) ratio ≤10th percentile for gestational age (GA), ponderal index (birth weight/length3 × 100) ≥90th percentile for GA, and macrosomia (birth weight ≥90th percentile for GA), on the basis of growth standard development for our population. For the first part of the objective, logistic regression models were used to identify 75-g glucose load values as well as obstetric and demographic variables as markers for abnormal neonatal anthropometric characteristics. For the second part, the receiver operating characteristic (ROC) curve was performed for the 75-g glucose load values to determine the plasma glucose threshold value that yielded the highest combined sensitivity and specificity for the prediction of abnormal neonatal anthropometric characteristics. RESULTS—In both early and late periods, maternal age &gt;35 years was a predictor of neonatal CC/TC ratio ≤10th percentile and macrosomia, with fasting 75-g glucose load values being independent predictors of neonatal CC/TC ratio ≤10th percentile. In both periods, 1-h values gave a strong association with all abnormal neonatal anthropometric characteristics chosen as outcome measures, with maternal age &gt;35 years being an independent predictor for macrosomia. The 2-h, 75-g glucose load values were significantly associated in both periods with neonatal CC/TC ratio ≤10th percentile and ponderal index ≥90th percentile, whereas maternal age &gt;35 years was an independent predictor of both neonatal CC/TC ratio ≤10th percentile and macrosomia. In the ROC curves for the prediction of neonatal CC/TC ratio ≤10th percentile for GA in both early and late periods of pregnancy, inflection points were identified for a 1-h, 75-g glucose load threshold value of 150 mg/dl in the early period and 160 mg/dl in the late period. CONCLUSIONS—This study documented a significant association, seen even in the early period of pregnancy, between 1-h, 75-g glucose load values and abnormal neonatal anthropometric features, and provided evidence of a threshold relation between 75-g glucose load results and clinical outcome. Our results would therefore suggest the possibility of using a 75-g, 1-h oral glucose load as a single test for the diagnosis of GDM, adopting a threshold value of 150 mg/dl at 16–20 weeks and 160 mg/dl at 26–30 weeks

Federated Geometric Monte Carlo Clustering to Counter Non-IID Datasets

Federated learning allows clients to collaboratively train models on datasets that are acquired in different locations and that cannot be exchanged because of their size or regulations. Such collected data is increasingly non-independent and non- identically distributed (non-IID), negatively affecting training accuracy. Previous works tried to mitigate the effects of non- IID datasets on training accuracy, focusing mainly on non-IID labels, however practical datasets often also contain non-IID features. To address both non-IID labels and features, we propose FedGMCC1, a novel framework where a central server aggregates client models that it can cluster together. FedGMCC clustering relies on a Monte Carlo procedure that samples the output space of client models, infers their position in the weight space on a loss manifold and computes their geometric connection via an affine curve parametrization. FedGMCC aggregates connected models along their path connectivity to produce a richer global model, incorporating knowledge of all connected client models. FedGMCC outperforms FedAvg and FedProx in terms of convergence rates on the EMNIST62 and a genomic sequence classification datasets (by up to +63%). FedGMCC yields an improved accuracy (+4%) on the genomic dataset with respect to CFL, in high non-IID feature space settings and label incongruency