Correctness Of Programs Research Articles

3D chromatin maps of a brown alga reveal U/V sex chromosome spatial organization.

Nuclear three dimensional (3D) folding of chromatin structure has been linked to gene expression regulation and correct developmental programs, but little is known about the 3D architecture of sex chromosomes within the nucleus, and how that impacts their role in sex determination. Here, we determine the sex-specific 3D organization of the model brown alga Ectocarpus chromosomes at 2 kb resolution, by mapping long-range chromosomal interactions using Hi-C coupled with Oxford Nanopore long reads. We report that Ectocarpus interphase chromatin exhibits a non-Rabl conformation, with strong contacts among telomeres and among centromeres, which feature centromere-specific LTR retrotransposons. The Ectocarpus chromosomes do not contain large local interactive domains that resemble TADs described in animals, but their 3D genome organization is largely shaped by post-translational modifications of histone proteins. We show that the sex determining region (SDR)within the U and V chromosomes are insulated and span the centromeres andwe link sex-specific chromatin dynamics and gene expression levels to the 3D chromatin structure of the U and V chromosomes. Finally, we uncover the unique conformation of a large genomic region on chromosome 6 harboring an endogenous viral element, providing insights regarding the impact of a latent giant dsDNA virus on the host genome's 3D chromosomal folding.

Multris: Functional Verification of Multiparty Message Passing in Separation Logic

We introduce Multris, a separation logic for verifying functional correctness of programs that combine multiparty message-passing communication with shared-memory concurrency. The foundation of our work is a novel concept of multiparty protocol consistency, which guarantees safe communication among a set of parties, provided each party adheres to its prescribed protocol. Our concept of protocol consistency is inspired by the bottom-up approach for multiparty session types. However, by considering it in the context of separation logic instead of a type system, we go further in terms of generality by supporting new notions of implicit transfer of knowledge and implicit transfer of resources. We develop tactics for automatically verifying protocol consistency and for reasoning about message-passing operations in Multris. We evaluate Multris on a range of examples, including the well-known two- and three-buyer protocols, as well as a new verification benchmark based on Chang and Roberts's ring leader election protocol. To ensure the reliability of our work, we prove soundness of Multris w.r.t. a low-level channel semantics using the Iris framework in Coq.

GeQuPI: Quantum Program Improvement with Multi-Objective Genetic Programming

Processing quantum information poses novel challenges regarding the debugging of faulty quantum programs. Notably, the lack of accessible information on intermediate states during quantum processing, renders traditional debugging techniques infeasible. Moreover, even correct quantum programs might not be processable, as current quantum computers are limited in computation capacity. Thus, quantum program developers have to consider trade-offs between accuracy (i.e., probabilistically correct functionality) and computational cost of the proposed solutions. Manually finding sufficiently accurate and efficient solutions is a challenging task, even for quantum computing experts. To tackle these challenges, we propose a quantum program improvement framework for an automated generation of accurate and efficient solutions, coined Genetic Quantum Program Improver (GeQuPI). In particular, we focus on the tasks of debugging and optimization of quantum programs. Our framework uses techniques from quantum information theory and applies multi-objective genetic programming, which can be further hybridized with quantum-aware optimizers. To demonstrate the benefits of GeQuPI, it is applied to 47 quantum programs reused from literature and openly published libraries. The results show that our approach is capable of correcting faulty programs and optimize inefficient ones for the majority of the studied cases, showing average optimizations of 35% with respect to computational cost.

View-based axiomatic reasoning for the weak memory models PSO and SRA

Weak memory models describe the semantics of concurrent programs in modern multicore architectures. As these semantics deviate from the commonly assumed model of sequential consistency, reasoning techniques like Owicki-Gries-style proof calculi need to be adapted to specific memory models. To avoid having to design a new proof calculus for every new memory model, a uniform approach for axiomatic reasoning has recently been proposed. This approach bases reasoning on memory-model independent axioms about thread views and how they are changed by program actions like reads and writes. It allows to prove program correctness based on axioms only. Such proofs are valid for all memory models instantiating the axioms.In this paper, we study instantiations of the axioms for two memory models, the Partial Store Order (PSO) and the Strong Release Acquire (SRA) model. We see that both models fulfil all but one axiom, a different one though. For PSO, the missing axiom refers to message-passing abilities of memory models; for SRA, the missing axiom refers to the independence of actions on executing threads. We discuss the consequences of these missing axioms and illustrate the reasoning technique on a specific litmus test.

Proving Correctness of Parallel Implementations of Transition System Models

This article addresses the long-standing problem of program correctness for programs that describe systems of parallel executing processes. We propose a new method for proving correctness of parallel implementations of high-level models expressed as transition systems. The implementation language underlying the method is based on the concurrency model of actors and active objects. The method defines program correctness in terms of a simulation relation between the transition system that specifies the program semantics of the parallel program and the transition system that is described by the correctness specification. The simulation relation itself abstracts from the fine-grained interleaving of parallel processes by exploiting a global confluence property of the concurrency model of the implementation language considered in this article. As a proof of concept, we apply our method to the correctness of a parallel simulator of multicore memory systems.

A Monte Carlo Simulation Dataset of Radiation Field Parameters for a Simple Nuclear Facility Scenario

Radioactive safety in nuclear facilities is of utmost importance. Prior to workers entering these areas, a 3D radiation field is needed for accurately estimating their exposure. Due to the complex relationship between radiation measurements and radiation fields, implementing neural networks is a promising approach for reconstruction. However, research on direct 3D radiation field reconstruction using neural networks is limited, and there is no standardized open-source dataset for training and evaluation. To address these issues, we created a simplified model of a nuclear facility and utilized the Monte Carlo program MCShield to simulate 3D radiation parameters. MCShield, which is mainly used for shielding calculations, has been verified for accuracy through benchmark tests. In addition, this paper proves the correctness of the MCShield program and the effectiveness of the AIS variance reduction method through calculations on the WinFrith Iron benchmark experiment and the NUREG/CR-6115 benchmark. The results show that the MCShield program as well as the AIS method can be used for dataset calculations.

Major evidence of nutritional and metabolic management in the context of bariatric surgery: a systematic review

Introduction: Approximately $315 billion is spent annually on the medical cost of obesity in adult patients in the United States alone. According to the American Society for Metabolic and Bariatric Surgery (ASMBS), the rate of bariatric surgery (BS) increased from 158,000 in 2011 to 196,000 in 2015. This increase in invasive techniques does not eliminate unhealthy habits, therefore, lifestyle modifications, such as healthy eating and correct physical activity programs, can improve surgical results. Objective: To establish the main nutritional and metabolic management strategies in the context of bariatric surgery, to modulate and reduce the problems caused by nutrient deficit. Methods: The systematic review rules of the PRISMA Platform were followed. The search was conducted from May to June 2024 in the Web of Science, Scopus, PubMed, Science Direct, Scielo, and Google Scholar databases. The quality of the studies was based on the GRADE instrument and the risk of bias was analyzed according to the Cochrane instrument. Results and Conclusion: A total of 135 articles were found. 42 articles were fully evaluated and 18 were included and developed in the present systematic review study. Considering the Cochrane tool for risk of bias, the overall assessment resulted in 22 studies with a high risk of bias and 24 that did not meet GRADE and AMSTAR-2. Most studies presented homogeneity in their results, with X2=79.7%>50%. It was concluded that the recommendations were gathered to assist in individualized clinical practice in the nutritional management of patients with obesity. In general, patients with obesity have significantly lower concentrations of serum iron, folic acid, vitamins A, B6, B12, C, 25-hydroxyvitamin D, and lipid-standardized vitamin E. Before bariatric surgery, nutritional status should be analyzed and preoperative weight loss can be attempted. Very low-calorie diets and very low-calorie ketogenic diets are prescribed in the last months before surgery. It was noted that the recommendations were gathered to assist in individualized clinical practice in the nutritional management of patients with obesity, including nutritional management. Iron status may be affected by adipose tissue inflammation and increased expression of the systemic iron regulatory protein hepcidin. The postoperative recommendation for vitamin B12 (cobalamin) should be 350-500 micrograms/1000 micrograms monthly, and for folate (folic acid) in the postoperative period should be 1000 micrograms per day.

On the expressiveness and efficiency of guarded lists in Bach

Concurrency theory has received considerable attention, but mostly in the scope of synchronous process algebras such as CCS, CSP, and ACP. As another way of handling concurrency, data-based coordination languages aim to provide a clear separation between interaction and computation by synchronizing processes asynchronously by means of information being available or not on a shared space. Although these languages enjoy interesting properties, verifying program correctness remains challenging. In particular, model checking logic formulae is known to raise performance issues due to the state space explosion problem. In this paper, we propose a guarded list construct as a solution to address this problem. Beyond increasing performance, it also enriches the expressiveness of data-based coordination languages and allows for program transformations that further increase the speed of computations.

The importance of monitoring neurological manifestations in rehabilitation after radical hysterectomy

Background. Hysterectomy is currently one of the most frequently performed surgical operations in obstetrics and gynecology, second only to cesarean section. However, such intervention is often associated with the risk of complications, including neurological ones. Neurological consequences, due to their characteristics, go beyond physical manifestations and exert psycho-emotional pressure on patients.Objective: to determine the need for monitoring neurological manifestations in women undergoing rehabilitation after hysterectomy.Material and methods. The search and selection of studies in Russian and English languages were conducted according to PRISMA recommendations in scientific databases and electronic libraries: PubMed/MEDLINE, ScienceDirect, Google Scholar, Microsoft Academic, DOAJ, eLibrary, CiberLeninka. In total, 2569 publications were revealed; after subsequent screening 57 articles were selected, which were devoted to the study of neurological adverse effects of hysterectomy and met all the screening criteria.Results. Among all the neurological complications mentioned, the most common is chronic postoperative pain. It is detected in 30% of cases. At the same time, in the vast majority of cases, such pains are rare and are tolerated as mild discomfort. However, there is no unambiguous understanding of the nature of this complication. There is a theory about the essential role of estrogen production and substance P ratio in pain manifestations. Thus, a decrease in estrogen production with unchanged synthesis of substance P significantly reduces the pain threshold in patients, which is the cause of the observed chronic pain. A certain contribution to the formation of this neurological complication is made by residual ovarian syndrome, which is associated with incomplete removal of the ovaries during ovariectomy, traumatic neuroma and mononervopathy. Damage to the nerves or their plexuses in the pelvic area can cause intestinal and urinary disorders, leading to a decrease in the quality of life of patients. Excessive anxiety and depression are additional risk factors for the development of chronic pain after hysterectomy, and the accompanying postoperative changes often have a physical manifestation, for example, in the form of insomnia, restless legs syndrome and sexual dysfunction.Conclusion. Hysterectomy is certainly an effective way to treat a number of gynecological diseases, but it often causes complications that require correction of the rehabilitation program. Neurological negative manifestations need special monitoring, since the depressive state of patients may lead to chronicity of pathological processes.

Imperative Genetic Programming

Genetic programming (GP) has a long-standing tradition in the evolution of computer programs, predominantly utilizing tree and linear paradigms, each with distinct advantages and limitations. Despite the rapid growth of the GP field, there have been disproportionately few attempts to evolve ’real’ Turing-like imperative programs (as contrasted with functional programming) from the ground up. Existing research focuses mainly on specific special cases where the structure of the solution is partly known. This paper explores the potential of integrating tree and linear GP paradigms to develop an encoding scheme that universally supports genetic operators without constraints and consistently generates syntactically correct Python programs from scratch. By blending the symmetrical structure of tree-based representations with the inherent asymmetry of linear sequences, we created a versatile environment for program evolution. Our approach was rigorously tested on 35 problems characterized by varying Halstead complexity metrics, to delineate the approach’s boundaries. While expected brute-force program solutions were observed, our method yielded more sophisticated strategies, such as optimizing a program by restricting the division trials to the values up to the square root of the number when counting its proper divisors. Despite the recent groundbreaking advancements in large language models, we assert that the GP field warrants continued research. GP embodies a fundamentally different computational paradigm, crucial for advancing our understanding of natural evolutionary processes.

PRINCIPLES OF TEACHER TRAINING IN THE FINNISH EDUCATION SYSTEM

The article presents in detail the principles of the Finnish educational policy, which are the basis of the principles of school management and teacher training. It presents the principles that are widely used in the process of teacher training in developed European countries and the principles that are characteristic mainly of the Finnish education system. We reveal the peculiarities of the principles of Finnish teacher training by presenting the theories of adaptation of education and masterful learning based on them. The paper analyzes the principles of equality, continuity, teaching evaluation, teaching research, and trust at the same time. It presents the current stage of the teacher training process in the education system in Finland, as well as the factors that form the principles of teacher training, in particular the innovative policy of Finland in the field of education. The article analyzes the principles of systematization, consistency of state policy, strategic direction of reforms, taking into account the socio-economic goals of the country, correction and modernization of programs. It analyses as a special feature, adaptive learning theories, examines in the context of which evaluation approaches and standards. It singles out the principle of individual development of Finnish teacher training. It is noted that it acts as an alternative principle. The idea is emphasized that any principles of Finnish teacher training are based on the principles of educational reforms, which are the starting point in educational policy. The novelty of the research is that, as a result of historical comparative analysis, trends and features of the formation and development of Finnish teacher training principles were revealed.

An automated OpenMP mutation testing framework for performance optimization

Performance optimization continues to be a challenge in modern HPC software. Existing performance optimization techniques, including profiling-based and auto-tuning techniques, fail to indicate program modifications at the source level thus preventing their portability across compilers. This paper describes Muppet, a new approach that identifies program modifications called mutations aimed at improving program performance. Muppet’s mutations help developers reason about performance defects and missed opportunities to improve performance at the source code level. In contrast to compiler techniques that optimize code at intermediate representations (IR), Muppet uses the idea of source-level mutation testing to relax correctness constraints and automatically discover optimization opportunities that otherwise are not feasible using the IR. We demonstrate the Muppet’s concept in the OpenMP programming model. Muppet generates a list of OpenMP mutations that alter the program parallelism in various ways, and is capable of running a variety of optimization algorithms such as delta debugging, Bayesian Optimization and decision tree optimization to find a subset of mutations which, when applied to the original program, cause the most speedup while maintaining program correctness. When Muppet is evaluated against a diverse set of benchmark programs and proxy applications, it is capable of finding sets of mutations that induce speedup in 75.9% of the evaluated programs.

ESTARK: extending STARKs with arguments

STARK is a widely used transparent proof system that uses low-degree tests for proving the correctness of a computer program. STARK consumes an intermediate representation known as AIR that is more appropriate for programs with a relatively short and structured description. However, an AIR is not able to succinctly express non-equality constraints, leading to the incorporation of unwanted polynomials. We present the eSTARK protocol, a new probabilistic proof that generalizes the STARK family through the introduction of a more generic intermediate representation called eAIR. We describe eSTARK in the polynomial IOP model, which combines the optimized version of the STARK protocol with the incorporation of three arguments into the protocol. We also explain various techniques that enhance the vanilla STARK complexity, including optimizations applied to polynomial computations, and analyze the tradeoffs between controlling the constraint degree either at the representation of the AIR or inside the eSTARK itself.

Fault localization by abstract interpretation and its applications

Fault localization aims to automatically identify the cause of an error in a program by localizing the error to a relatively small part of the program. In this paper, we present a novel technique for automated fault localization via error invariants inferred by abstract interpretation. An error invariant for a location in an error program over-approximates the reachable states at the given location that may produce the error, if the execution of the program is continued from that location. Error invariants can be used for statement-wise semantic slicing of error programs and for obtaining concise error explanations. We use an iterative refinement sequence of backward–forward static analyses by abstract interpretation to compute error invariants, which are designed to explain why an error program violates a particular assertion.Furthermore, we present a practical application of the fault localization technique for automatic repair of programs. Given an erroneous program, we first use the fault localization to automatically identify statements relevant for the error, and then repeatedly mutate the expressions in those relevant statements until a correct program that satisfies all assertions is found. All other statements classified by the fault localization as irrelevant for the error are not mutated in the program repair process. This way, we significantly reduce the search space of mutated programs without losing any potentially correct program, and so locate a repaired program much faster than a program repair without fault localization.We have developed a prototype tool for automatic fault localization and repair of C programs. We demonstrate the effectiveness of our approach to localize errors in realistic C programs, and to subsequently repair them. Moreover, we show that our approach based on combining fault localization and code mutations is significantly faster that the previous program repair approach without fault localization.

Analyzing novice and competent programmers' problem-solving behaviors using an automated evaluation system

Background and ContextIn today's tech-driven world, programming courses are crucial. Yet, teaching programming is challenging, leading to high student failure rates. Understanding student learning patterns is key, but there's a lack of research utilizing tools to automatically collect and analyze interaction data for insights into student performance and behaviors. ObjectivesStudy aims to compare problem-solving behaviors of novice and competent programmers during coding tests, identifying patterns and exploring relationships with program correctness. MethodWe built an online system with programming challenges to collect behavior data from novice and competent programmers. Our system analyzed data using various metrics to explore behavior-program correctness relationships. FindingsAnalysis showed distinct problem-solving behavior patterns. Competent programmers had fewer syntax errors, spent less time fixing bugs, and had higher program correctness. Novices made more syntax errors and spent more time fixing coding errors. Both groups used tabs for code structure, but competent programmers introduced unfamiliar variables more often and commented on them afterward. Emphasizing iterative revisions and active engagement enhances problem-solving skills and programming proficiency. Radar charts are effective for identifying improvement areas in teaching programming. The relationship between behavior and program correctness was positively correlated for competent programmers but not novices. ImplicationsStudy findings have implications for programming education. Radar charts help teachers identify course improvement areas. Novices can learn from competent programmers' behavior. Instructors should encourage continuous skill improvement through revisions and engagement. Identified unfamiliar programming aspects offer insights for targeted learning.

CYCLE: Learning to Self-Refine the Code Generation

Pre-trained code language models have achieved promising performance in code generation and improved the programming efficiency of human developers. However, their self-refinement capability is typically overlooked by the existing evaluations of code LMs, which focus only on the accuracy of the one-time prediction. For the cases when code LMs fail to implement the correct program, developers actually find it hard to debug and fix the faulty prediction since it is not written by the developers themselves. Unfortunately, our study reveals that code LMs cannot efficiently self-refine their faulty generations as well. In this paper, we propose CYCLE framework, learning to self-refine the faulty generation according to the available feedback, such as the execution results reported by the test suites. We evaluate CYCLE on three popular code generation benchmarks, HumanEval, MBPP, and APPS. The results reveal that CYCLE successfully maintains, sometimes improves, the quality of one-time code generation, while significantly improving the self-refinement capability of code LMs. We implement four variants of CYCLE with varied numbers of parameters across 350M, 1B, 2B, and 3B, and the experiments show that CYCLE consistently boosts the code generation performance, by up to 63.5

Correction Control Model of L-Index Based on VSC-OPF and BLS Method

With the advancement of artificial intelligence (AI) technology, the real-time measurement and control technology of power systems has also progressed. This paper proposes a correction control model for L-indexes based on voltage stability constrained optimal power flow (VSC-OPF) and a broad learning system (BLS) (BLS-VSC-OPF). This model aims to quickly assess the system’s voltage stability and accurately correct the operation mode when the voltage stability indexes are out of the security range. Firstly, the BLS is used to predict the L-index and to analyze the voltage stability of the power system. Secondly, the approximate first-order sensitivity of the L-index is calculated by the combination of the BLS and the perturbation method. This method solves the problem of the complex sensitivity derivation process in the modeling process of the VSC-OPF model. Meanwhile, when the L-index exceeds the threshold, the BLS and VSC-OPF models are combined to correct this operation mode. The feasibility of the proposed method is verified by the simulation of IEEE-30, IEEE-118, and 1047 bus systems. Finally, the BLS-VSC-OPF model is compared with the linear programming correction model based on BLS (BLS-LPC). The results show that the BLS-VSC-OPF model provides a better correction and control performance.

Clinical and Psychological Characteristics of Patients after First-Episode Psychosis with Cognitive, Emotional, and Volitional Deficiencies: Comparative Analysis

This research featured 139 patients (60 men, 79 women) after the first-episode psychosis. All patients demonstrated cognitive and/or emotional deficiency. The research objective was to compare clinical, sociodemographic, psychosocial, and individual characteristics. The expert clinical assessment revealed three groups. Group I included 29 patients with negative symptoms in the cognitive sphere; group II consisted of 46 people with negative symptoms in the emotional and volitional sphere; group III included 64 people with no pronounced deficiencies. The authors employed the method of clinical psychological interview and various pathopsychological non-standard methods to study the cognitive activity of the patients. The list of psychometric methods included the Beck’s Depression Inventory, a coping behavior questionnaire, a short version of Minnesota Multiphasic Personality Inventory (MMPI), and the Suicide Risk Assessment Scale. The data obtained were processed using SPSS v. 25.0 and Excel 2010. The three groups demonstrated the following statistically significant differences: diagnosis, critical attitude to one’s own condition, and continuity of psychiatric care (clinical characteristics); social adaptation problems (psychosocial characteristics); association and motivation (cognitive activity); suicidal risk (emotions, personality, and behavior). The MMPI and the coping behavior test also showed differences from the test norm, as well as between the groups. A comparative analysis of clinical and psychological characteristics in patients after the first-episode psychosis makes it possible to develop a correct psychorehabilitation program, prevent deficit symptoms, and improve social functioning.

An ensemble learning interpretation of geometric semantic genetic programming

AbstractGeometric semantic genetic programming (GSGP) is a variant of genetic programming (GP) that directly searches the semantic space of programs to produce candidate solutions. GSGP has shown considerable success in improving the performance of GP in terms of program correctness, however this comes at the expense of exponential program growth. Subsequent attempts to address this growth have not fully-exploited the fact that GSGP searches by producing linear combinations of existing solutions. This paper examines this property of GSGP and frames the method as an ensemble learning approach by redefining mutation and crossover as examples of boosting and stacking, respectively. The ensemble interpretation allows for simple integration of regularisation techniques that significantly reduce the size of the resultant programs. Additionally, this paper examines the quality of parse tree base learners within this ensemble learning interpretation of GSGP and suggests that future research could substantially improve the quality of GSGP by examining more effective initialisation techniques. The resulting ensemble learning interpretation leads to variants of GSGP that substantially improve upon the performance of traditional GSGP in regression contexts, and produce a method that frequently outperforms gradient boosting.

Cerise: Program Verification on a Capability Machine in the Presence of Untrusted Code

A capability machine is a type of CPU allowing fine-grained privilege separation using capabilities , machine words that represent certain kinds of authority. We present a mathematical model and accompanying proof methods that can be used for formal verification of functional correctness of programs running on a capability machine, even when they invoke and are invoked by unknown (and possibly malicious) code. We use a program logic called Cerise for reasoning about known code, and an associated logical relation, for reasoning about unknown code. The logical relation formally captures the capability safety guarantees provided by the capability machine. The Cerise program logic, logical relation, and all the examples considered in the paper have been mechanized using the Iris program logic framework in the Coq proof assistant. The methodology we present underlies recent work of the authors on formal reasoning about capability machines [Georges et al. 2021 ; Skorstengaard et al. 2019a ; Van Strydonck et al. 2022 ], but was left somewhat implicit in those publications. In this paper we present a pedagogical introduction to the methodology, in a simpler setting (no exotic capabilities), and starting from minimal examples. We work our way up to new results about a heap-based calling convention and implementations of sophisticated object-capability patterns of the kind previously studied for high-level languages with object-capabilities, demonstrating that the methodology scales to such reasoning.