Verifying Functional Correctness Properties at the Level of Java Bytecode

Evolving pragmatics

The imaginary-time Green's function is a building block of various numerical methods for correlated electron systems. Recently, it was shown that a model-independent compact orthogonal representation of the Green's function can be constructed by decomposing its spectral representation. We investigate the performance of this so-called intermediate representation (IR) from several points of view. First, we develop an efficient algorithm for computing the IR basis functions of arbitrary high degree. Second, for two simple models, we study the number of coefficients required to represent the Green's function within a given tolerance. We show that the number of coefficients grows only as $O(ln\ensuremath{\beta})$ for fermions and converges to a constant for bosons as temperature $T=1/\ensuremath{\beta}$ decreases. Third, we show that this remarkable feature is ascribed to the properties of the physically constructed basis functions. The fermionic basis functions on the real-frequency axis have features whose width is scaled as $O(T)$, which is consistent with the low-$T$ properties of quasiparticles in a Fermi liquid state. On the other hand, the properties of the bosonic basis functions are consistent with those of spin/orbital susceptibilities at low $T$. These results demonstrate the potential wide applications of the IR to calculations of correlated systems.

It is usually assumed that modern language is a recent phenomenon, coinciding with the emergence of modern humans themselves. Many assume as well that this is the result of a single, sudden mutation giving rise to the full “modern package.” However, we argue here that recognizably modern language is likely an ancient feature of our genus pre-dating at least the common ancestor of modern humans and Neandertals about half a million years ago. To this end, we adduce a broad range of evidence from linguistics, genetics, paleontology, and archaeology clearly suggesting that Neandertals shared with us something like modern speech and language. This reassessment of the antiquity of modern language, from the usually quoted 50,000–100,000 years to half a million years, has profound consequences for our understanding of our own evolution in general and especially for the sciences of speech and language. As such, it argues against a saltationist scenario for the evolution of language, and toward a gradual process of culture-gene co-evolution extending to the present day. Another consequence is that the present-day linguistic diversity might better reflect the properties of the design space for language and not just the vagaries of history, and could also contain traces of the languages spoken by other human forms such as the Neandertals.

The purpose of this study is to locate the Sinhala language in the South Asian linguistic area and the Indo-Aryan subgroup and to examine the concepts and ideas related to the origin and evolution of the Sinhala language. The data were collected using document analysis and analysed using content analysis. Building the relevant rationale by analysing and synthesising the results of previous studies is the study path followed here. According to the findings, it is evident that the identity of the Sinhala language is the result of a different evolutionary path than other languages belonging to the Indo-Aryan sub-group of the Indo-European language family. Since Sinhala began to exhibit its independent aspect before the beginning of the 'Modern Indo-Aryan languages', it is justified to consider the Sinhala language as a modern language that emerged from the contact of a Middle Indo-Aryan dialect with the Old Indo-Aryan languages and the languages belong to other language families such as Dravidian and Sino-Tibetan. Although Sanskrit and the language known as 'Pali' are recognised as the 'mother languages' of Sinhala, there is no evidence that Sinhala was initiated as a dialect of either Sanskrit or 'Pali'. According to the historical analysis, several Prakrit vernaculars led to the source of the Vedic language, which was developed and modified as Sanskrit during 5c. B.C. was isolated from the original mainland and developed independently on this island. The mixing of Prakrit dialects on this island and the contact with the languages of the early inhabitants of the island was the first step in the independent aspect of the Sinhala and the contact with the colloquial languages of other nations arrived here due to various socio-economic, political, and cultural relationships led to the preparation of the new face of Sinhala. The origin and evolution of the Sinhala language took place through admixture with other non-Dravidian languages - mainly Tamil, the primary language of the Dravidian language family. The reasons for finding a vocabulary similar to Sanskrit and Pali languages - which are considered to have evolved from them - are that the Prakrit verículars based on those languages are rooted in the source of Sinhala and the development of Sinhala language due to socio-cultural (educational and religious etc.) reasons similar to Sanskrit and Pali languages. It is clear that the use of loan words and a derived vocabulary and the influence of the grammatical features of those languages.

This paper discusses the role of interactional and cognitive mechanisms in the emergence of (proto)linguistic structures and the evolution of (proto)language(s). Both the social, interactive nature of human communication and the interactional timescale have received increasing attention in investigations of how structure emerges in language. This has also led to an increasing focus on the mechanisms involved in the dialogic co-construction of structure and meaning in interaction. These include ad hoc constructionalization, interactive alignment, conceptual pacts, reuse and modification, and local forms of entrenchment, routinisation and schematisation. Interactional and cognitive mechanisms like these do not only play a crucial role in the emergence of structure in modern languages. They can also help explain how the first (proto)constructions came into being in hominin interaction. Frequently re-occuring, temporary, local (proto)constructions acquired increasing degrees of entrenchment, which led to their subsequent diffusion throughout hominin communities. They were then subject to processes of conventionalisation and cumulative cultural evolution. This process is hypothesised to eventually have led to the gradual transition from protolanguage to language.

This article discusses the significance of the emergence of language. One of Maynard Smith and Eörs Szathmáry's observations is that, despite their diversity, these transitions have some features in common. In particular, many of the transitions give rise to a new mechanism for the transmission of information. Language, they argue, provides just such a novel mechanism — essentially enabling a system of cultural transmission with unlimited heredity. It is clearly true that language enables the transmission and storage of very complex cultural information. Arguably, it is this aspect of the biological heritage that makes the impact of humans so great, and so unusual. The article argues that human language achieves this by briefly surveying the structural features of language, and the characteristics of language as a biological endowment.

Language evolution refers to the origins and development of the human system of communication. Research on language evolution compares aspects of human communication with those of other animals, especially nonhuman primates, and analyzes gestures, multimodal communication, pidgins and creoles, child development, the physiology of the vocal tract, genes and neurons, the brain and brain damage, as well as the fossil record. Theories of language evolution discuss whether human language emerged gradually or abruptly and through which evolutionary adaptations or exaptations, as well as whether there are any key features of human language. Controversies remain but recent research has contributed to a more nuanced picture of differences between humans and nonhumans that may prove to not be qualitative but quantitative.

Code clones are widespread in software since programmers always reuse code to reduce programming effort. As programming languages are continuing to evolve and morph, code clones also widely exist across different languages for platform compatibility and adoption. Although code clones can improve development efficiency, they are prone to introducing bugs. Existing code clone detection technologies, however, mainly focused on single programming language or syntactical features of code. The syntax of different programming language are diverse because of syntax sugar, and many cloning pairs are semantic related instead of syntactic similar, such as Type 4 clones. To bridge the gap between syntax and semantic, and detect clone-related bugs more accurately, we explore an IR (Intermediate Representation) based method to represent code semantic representation information of multiple language code. We utilize graph neural network to learn code semantic representation. Through the semantic representation, we can detect more cross-language clone related bugs across multiple language.

Rust is gaining popularity for its well-known memory safety guarantees and high performance, distinguishing it from C/C++ and JVM-based languages. Its compiler, rustc, enforces these guarantees through specialized mechanisms such as trait solving, borrow checking, and specific optimizations. However, Rust's unique language mechanisms introduce complexity to its compiler, resulting in bugs that are uncommon in traditional compilers. With Rust's increasing adoption in safety-critical domains, understanding these language mechanisms and their impact on compiler bugs is essential for improving the reliability of both rustc and Rust programs. Such understanding could provide the foundation for developing more effective testing strategies tailored to rustc. Improving the quality of rustc testing is essential for enhancing compiler reliability, which in turn strengthens the safety and correctness of all Rust programs, as compiler bugs can silently propagate into every compiled program. Yet, we still lack a large-scale, detailed, and in-depth study of rustc bugs. To bridge this gap, this work presents a comprehensive and systematic study of rustc bugs, specifically those originating in semantic analysis and intermediate representation (IR) processing, which are stages that implement essential Rust language features such as ownership and lifetimes. Our analysis examines issues and fixes reported between 2022 and 2024, with a manual review of 301 valid issues. We categorize these bugs based on their causes, symptoms, affected compilation stages, and test case characteristics. Additionally, we evaluate existing rustc testing tools to assess their effectiveness and limitations. Our key findings include: (1) rustc bugs primarily arise from Rust's type system and lifetime model, with frequent errors in the High-Level Intermediate Representation (HIR) and Mid-Level Intermediate Representation (MIR) modules due to complex checkers and optimizations; (2) bug-revealing test cases often involve unstable features, advanced trait usages, lifetime annotations, standard APIs, and specific optimization levels; (3) while both valid and invalid programs can trigger bugs, existing testing tools struggle to detect non-crash errors, underscoring the need for further advancements in rustc testing.

      The current research is an attempt to present the use of poetic language in modern poetry in Ramazan Issa`s poems, the collection of " falsafa barxwdanye" as a model. To embody the characteristics of modern poetry language used in the  mentioned above collection, light has been sheet on the ambiguous aspects in his poems, and the importance of language elements. This research is titled " The poetic language in Ramazan Issa`s poems: Falsafa Barxwdanye  as a model". We have presented the notion of the characteristics of modern poetry language from its structural aspects;  and explained the concept of modern poetic language. Moreover, we have showed the basic elements of  his writing styles in order to shed light on all these elements are reflected in the texts as a  means for his creativity. In this research, we have also explained the explicit and implicit expressions. As a result, it has been found that the poet uses an amazing writing style in his modern poetic language.

An icon description language, named DSIL (Data Structure Iconic Language), is described which facilitates the design of icons depicting data structures and accelerates the development of iconic interfacing tools for program visualization and debugging. By using the DSIL, different users can define or redefine their own icons to satisfy their needs. The DSIL has features of modern programming languages. It is hierarchical, modular, and provides adoption (inheritance). The design of the DSIL is based on the features of data structures in MODULA-2. The implementation of the DSIL in a data structure editor is presented.

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.

Aspect languages for Smalltalk have not kept up with advances in aspect language research. Arguably the only well-known aspect language for Smalltalk is AspectS. It is a ground-breaking contribution, especially regarding dynamic aspects, yet it lacks amenities which aspect language users have come to rely on, e.g. the use of patterns in pointcuts and the ability to declare aspect precedence. Alternative aspect languages for Smalltalk are effectively absent. As a result, currently Smalltalk lacks a modern and powerful aspect language.To address this deficit, we elected to design and build PHANtom: a modern aspect language for Pharo Smalltalk. PHANtom is designed to be an aspect language in the spirit of Smalltalk: dynamic, simple and powerful. PHANtom is a modern aspect language because it incorporates what we consider to be the best features of languages that precede it, includes recent research results in aspect interactions and reentrancy control, and is designed from the onset to be optimized and compiled where possible. This demo presents PHANtom by first providing an introduction to the language, detailing its philosophy and fundamental features. It second discusses the salient features of the language by demonstrating the use of PHANtom in an example application.

Language change across space and time is one of the main concerns in historical linguistics. In this article, we develop tools to assist researchers and domain experts in the study of language evolution. First, we introduce a method to automatically determine whether two words are cognates. We propose an algorithm for extracting cognates from electronic dictionaries that contain etymological information. Having built a data set of related words, we further develop machine learning methods based on orthographic alignment for identifying cognates. We use aligned subsequences as features for classification algorithms in order to infer rules for linguistic changes undergone by words when entering new languages and to discriminate between cognates and non-cognates. Second, we extend the method to a finer-grained level, to identify the type of relationship between words. Discriminating between cognates and borrowings provides a deeper insight into the history of a language and allows a better characterization of language relatedness. We show that orthographic features have discriminative power and we analyze the underlying linguistic factors that prove relevant in the classification task. To our knowledge, this is the first attempt of this kind. Third, we develop a machine learning method for automatically producing related words. We focus on reconstructing proto-words, but we also address two related sub-problems, producing modern word forms and producing cognates. The task of reconstructing proto-words consists of recreating the words in an ancient language from its modern daughter languages. Having modern word forms in multiple Romance languages, we infer the form of their common Latin ancestors. Our approach relies on the regularities that occurred when words entered the modern languages. We leverage information from several modern languages, building an ensemble system for reconstructing proto-words. We apply our method to multiple data sets, showing that our approach improves on previous results, also having the advantage of requiring less input data, which is essential in historical linguistics, where resources are generally scarce.

Contrary to the widespread notion that linguistic signs are arbitrary, researchers have consistently demonstrated the existence of sound symbolism in language, providing evidence for non-arbitrariness in sound-meaning associations. However, much evidence of this kind is based on a limited subset of vocabulary and falls short of systematically demonstrating the pervasive nature of sound symbolism and, especially, its central, rather than marginal, role in language. Furthermore, a historical perspective is lacking to determine whether sound symbolism is merely a feature of archaic languages or has remained a significant element throughout the evolution of languages. This research pioneers a diachronic analysis of sound symbolism in Chinese using historical rhyme books to trace its presence on the vocabulary scale. Employing natural language processing techniques along with statistical methods, it investigates whether phonologically related Chinese characters, as documented in rhyme books, also demonstrate semantic congruence, which would suggest that the phonological aspects of characters are inherently meaningful and hence indicate a systematic, rather than random or purely arbitrary relationship between sounds and meanings. Statistically significant results from our analysis of all four analyzed rhyme books confirm the robustness of sound symbolism over a large span of the Chinese language continuum, and a granular analysis of a representative one of them further reveals that sound symbolism is manifest across various levels of phonological organization, including initials, finals, etc. This study initiates an innovative combination of traditional materials with novel techniques to enrich and expand existing knowledge about sound symbolism, providing both methodological advancement and empirical insights.