Towards A Modular End-To-End Machine Learning Benchmarking Framework

Machine learning (ML) benchmarks are crucial for evaluating the performance, efficiency, and scalability of ML systems, especially as the adoption of complex ML pipelines, such as retrieval-augmented generation (RAG), continues to grow. These pipelines introduce intricate execution graphs that require more advanced benchmarking approaches. Additionally, collocating workloads can improve resource efficiency but may introduce contention challenges that must be carefully managed. Detailed insights into resource utilization are necessary for effective collocation and optimized edge deployments. However, existing benchmarking frameworks often fail to capture these critical aspects.We introduce a modular end-to-end ML benchmarking framework designed to address these gaps. Our framework emphasizes modularity and reusability by enabling reusable pipeline stages, facilitating flexible benchmarking across diverse ML workflows. It supports complex workloads and measures their end-to-end performance. The workloads can be collocated, with the framework providing insights into resource utilization and contention between the concurrent workloads

The Cultural Complexity of Carbon

This volume discusses the transformational role that carbon – both as a concept and as a distinct set of material forms and effects – has come to play in social and cultural life.As a proxy for greenhouse gas emission data, carbon has grown to become a phenomenon that can no longer be accounted for solely within the technoscientific vocabulary of climate scientists. The Cultural Complexity of Carbon examines the extent to which our knowledge of carbon affects the way that human beings relate to each other and to the climate and/or the environment. It draws on case studies from a diverse range of topics including peatland restoration, religion and energy systems to explore questions that have so far been under-explored in the current literature. These questions include whether the recognition of carbon’s role in climate change leads to an incremental adaptation of lifestyles or to cultural or existential transformations, but also more concretely how carbon is made meaningful, and how these meanings are attached to ideals of cultural change or continuity. Spanning multiple perspectives and disciplinary positions, this volume provides a go-to point for the next generation of ethnographic studies of carbon and climate change. It cuts across what has hitherto been largely separate literatures in anthropology, geography and sociology to provide a meta-level orientation to how contemporary narratives of the role of carbon are being told.By addressing the intimate social and cultural changes that stem from humanity’s involvement with its natural and climatic resources, this volume is of interest to students and scholars of climate change within the social sciences and environmental humanities

The AI Gap: How Socioeconomic Status Affects Language Technology Interactions

Socioeconomic status (SES) fundamentally influences how people interact with each other and, more recently, with digital technologies like large language models (LLMs). While previous research has highlighted the interaction between SES and language technology, it was limited by reliance on proxy metrics and synthetic data. We survey 1,000 individuals from ‘diverse socioeconomic backgrounds’ about their use of language technologies and generative AI, and collect 6,482 prompts from their previous interactions with LLMs. We find systematic differences across SES groups in language technology usage (i.e., frequency, performed tasks), interaction styles, and topics. Higher SES entail a higher level of abstraction, convey requests more concisely, and topics like ‘inclusivity’ and ‘travel’. Lower SES correlates with higher anthropomorphization of LLMs (using ”hello” and ”thank you”) and more concrete language. Our findings suggest that while generative language technologies are becoming more accessible to everyone, socioeconomic linguistic differences still stratify their use to create a digital divide. These differences underscore the importance of considering SES in developing language technologies to accommodate varying linguistic needs rooted in socioeconomic factors and limit the AI Gap across SES groups

Code for "Functional matrix product state simulation of continuous variable quantum circuits"

Code and reproduction notebooks for the publication "Functional matrix product state simulation of continuous variable quantum circuits". The code was run using Strawberry Fields release 0.23.0 Python release 3.12.9 Numpy release 2.0.2 Scipy release 1.13.

Functional Reactive GUI Programming with Modal Types.

Functional reactive programming (FRP) is a programming paradigm for implementing reactive systems, i.e. programs that continuously interact with their environments. While FRP allows for a functional, high-level programming style, FRP programs are prone to undesirable operational behaviours such as space leaks. To ensure favourable operational properties of FRP programs, modal type systems have been introduced, which – among other things – make it impossible to write FRP programs with implicit space leaks. In a recent development, several modal FRP languages have been introduced that are able to accommodate asynchronous events and behaviours – motivated by the goal to use such languages for GUI programming.This paper explores the suitability of one such asynchronous modal FRP language – called Async Rattus – for GUI programming in practice. To this end, we have implemented a mild extension of the Async Rattus language and used it to implement a small GUI framework. We demonstrate the language and its GUI framework by a number of case studies