Recent advances in Bayesian probability theory and its application to cognitive science in combination with the development of a new generation of computational tools and methods for probabilistic computation have led to a 'probabilistic turn' in pragmatics and semantics. In particular, the framework of Rational Speech Act theory has been developed to model broadly Gricean accounts of pragmatic phenomena in Bayesian terms, starting with fairly simple reference games and covering ever more complex communicative exchanges such as verbal syllogistic reasoning. This paper explores in which way a similar Bayesian approach might be applied to relevance-theoretic pragmatics (Sperber & Wilson, 1995) by study a paradigmatic pragmatic phenomenon: the communication of implicit meaning by ways of (conversational) implicatures.

Federated Learning (FL) is critical for edge and High Performance Computing (HPC) where data is not centralized and privacy is crucial. We present OmniFed, a modular framework designed around decoupling and clear separation of concerns for configuration, orchestration, communication, and training logic. Its architecture supports configuration-driven prototyping and code-level override-what-you-need customization. We also support different topologies, mixed communication protocols within a single deployment, and popular training algorithms. It also offers optional privacy mechanisms including Differential Privacy (DP), Homomorphic Encryption (HE), and Secure Aggregation (SA), as well as compression strategies. These capabilities are exposed through well-defined extension points, allowing users to customize topology and orchestration, learning logic, and privacy/compression plugins, all while preserving the integrity of the core system. We evaluate multiple models and algorithms to measure various performance metrics. By unifying topology configuration, mixed-protocol communication, and pluggable modules in one stack, OmniFed streamlines FL deployment across heterogeneous environments. Github repository is available at https://github.com/at-aaims/OmniFed.

This paper proposes a framework for modeling multimodal conversational turn organization via the proposition of correlations between language and interactive gestures, based on analysis as to how pragmatic frames are conceptualized and evoked by communicators. As a means to provide evidence for the analysis, we developed an annotation methodology to enrich a multimodal dataset (annotated for semantic frames) with pragmatic frames modeling conversational turn organization. Although conversational turn organization has been studied by researchers from diverse fields, the specific strategies, especially gestures used by communicators, had not yet been encoded in a dataset that can be used for machine learning. To fill this gap, we enriched the Frame2 dataset with annotations of gestures used for turn organization. The Frame2 dataset features 10 episodes from the Brazilian TV series Pedro Pelo Mundo annotated for semantic frames evoked in both video and text. This dataset allowed us to closely observe how communicators use interactive gestures outside a laboratory, in settings, to our knowledge, not previously recorded in related literature. Our results have confirmed that communicators involved in face-to-face conversation make use of gestures as a tool for passing, taking and keeping conversational turns, and also revealed variations of some gestures that had not been documented before. We propose that the use of these gestures arises from the conceptualization of pragmatic frames, involving mental spaces, blending and conceptual metaphors. In addition, our data demonstrate that the annotation of pragmatic frames contributes to a deeper understanding of human cognition and language.

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Arabic Rhetoric is the field of Arabic linguistics which governs the art and science of conveying a message with greater beauty, impact and persuasiveness. The field is as ancient as the Arabic language itself and is found extensively in classical and contemporary Arabic poetry, free verse and prose. In practical terms, it is the intelligent use of word order, figurative speech and linguistic embellishments to enhance message delivery. Despite the volumes that have been written about it and the high status accorded to it, there is no way to objectively know whether a speaker or writer has used Arabic rhetoric in a given text, to what extent, and why. There is no objective way to compare the use of Arabic rhetoric across genres, authors or epochs. It is impossible to know which of pre-Islamic poetry, Andalucian Arabic poetry, or modern literary genres are richer in Arabic rhetoric. The aim of the current study was to devise a way to measure the density of the literary devices which constitute Arabic rhetoric in a given text, as a proxy marker for Arabic rhetoric itself. A comprehensive list of 84 of the commonest literary devices and their definitions was compiled. A system of identifying literary devices in texts was constructed. A method of calculating the density of literary devices based on the morpheme count of the text was utilised. Four electronic tools and an analogue tool were created to support the calculation of an Arabic text's rhetorical literary device density, including a website and online calculator. Additionally, a technique of reporting the distribution of literary devices used across the three sub-domains of Arabic rhetoric was created. The output of this project is a working tool which can accurately report the density of Arabic rhetoric in any Arabic text or speech.

The development of large-scale models relies on a vast number of computing resources. For example, the GPT-4 model (1.8 trillion parameters) requires 25000 A100 GPUs for its training. It is a challenge to build a large-scale cluster with a type of GPU-accelerator. Using multiple types of GPU-accelerators to construct a cluster is an effective way to solve the problem of insufficient homogeneous GPU-accelerators. However, the existing distributed training systems for large-scale models only support homogeneous GPU-accelerators, not heterogeneous GPU-accelerators. To address the problem, this paper proposes a distributed training system with hybrid parallelism support on heterogeneous GPU-accelerators for large-scale models. It introduces a distributed unified communicator to realize the communication between heterogeneous GPU-accelerators, a distributed performance predictor, and an automatic hybrid parallel module to develop and train models efficiently with heterogeneous GPU-accelerators. Compared to the distributed training system with homogeneous GPU-accelerators, our system can support six different combinations of heterogeneous GPU-accelerators and the optimal performance of heterogeneous GPU-accelerators has achieved at least 90% of the theoretical upper bound performance of homogeneous GPU-accelerators.

As the responsibility of software developers encompasses more than just writing code, current Large language models (LLMs) have significantly improved the ability LLMs cannot fully replace professional software developers [4, 29]. to perform tasks in the field of code generation. However, there At a high level, the gap lies in several critical aspects of software is still a gap between LLMs being capable coders and being top-tier development beyond coding, such as effective communications, software engineers. Based on the observation that top-level software requirements, design, domain knowledge, and the broader context engineers often ask clarifying questions to reduce ambiguity of relevant projects and components, etc. [23, 31, 32, 35]. In this in both requirements and coding solutions, we argue that the same paper, we are interested in applying the communication lens to should be applied to LLMs for code generation tasks. By asking inspect the gap, given that effective communication is a critical probing questions in various topics before generating the final code, capability that connects all of the above-mentioned parts to coding.

Several faculty members in CMS/W have expertise, both technological and pedagogical, in what the use of ChatGPT and other A.I. tools may mean for the instruction of academic writing. Because instructors at MIT and elsewhere have expressed some urgency in better understanding what the effects and ethics of ChatGPT may be, two pairs of our faculty have provided an advisory memo and a response. It should be noted that with tools like ChatGPT being both so new and so quickly evolving, these pieces are the faculty's take and don't yet represent official guidance from CMS/W or MIT. First is an excerpt from "Advice Concerning the Increase in AI-Assisted Writing", a memo from Edward Schiappa, Professor of Rhetoric, and Nick Montfort, Professor of Digital Media. The full document is available at Montfort's website.

Understanding of pragmatics is an essential and ubiquitous part of human communication. We show large language models (LLMs) mostly don't capture this aspect of language, hindering their applicability in the real world. Our analysis indicates where the largest room for improvement is to ultimately make this technology more useful. Recently, a large language model (LLM) called LaMDA beautifully passed (a variation of) the Turing test. In our most recent paper's title we state that LLMs are not zero-shot communicators.

Have you ever seen a bridge collapse on TV? We only care about inspection and maintenance if it does not work. However, robotics are changing the inspection & maintenance landscape, and significant societal and political implications follow the magnitude of innovation. To pay due attention to these developments, the communication on innovation in robotics for inspection & maintenance has to play a role in informing and influencing the target groups for the successful adoption of robotics technologies. The question on what role communications play in forming the perception of innovative technology was discussed in the workshop "Communicating innovation: What can we do better?" on May 25th, 2022.